Local setup and private inference
Installing Ollama, running models offline on personal hardware, and emphasizing data sovereignty, no API costs, and cloud independence.
60%
Best tweets about Ollama
50 Best Tweets About Ollama (2026)
Find the best tweets about Ollama, including local model setup, performance, hardware, integrations, model files, and developer workflows.
Hands-on Ollama setup, local inference, supported models, integrations, performance, troubleshooting, and releases.
- Creators
- 40
- Updated
Original Xholic analysis
What 50 top Ollama posts reveal
The conversation emphasizes private, local inference, hardware-aware model selection, and developer integrations. Posts also describe a hybrid pattern: local models for routine or private work, with stronger cloud models retained for demanding tasks.
- Dominant tone
- Positive
- Median score
- 16.3
- Leading format
- Announcement
- Recent posts
- 44%
70% of posts
All-time engagement
36% of posts
Published in 90 days
Conversation map
The themes creators return to
Coding agents and developer integrations
Using Ollama with Claude Code, OpenCode, Codex, VS Code/Copilot, OpenClaw, Hermes, MCP, and OpenAI/Anthropic-compatible endpoints.
52%
Model selection and quantization
Choosing Llama, Qwen, Gemma, DeepSeek, and specialist models by task and hardware, including quantization, context limits, and model-size tradeoffs.
38%
Hardware sizing and inference performance
RAM/VRAM and unified-memory guidance, device comparisons, token-speed benchmarks, latency, cache behavior, and backend performance.
34%
Releases and API compatibility
Ollama version updates, model availability, cloud hardware updates, Anthropic/OpenAI API compatibility, and newly supported workflows.
24%
Specialist and multimodal local models
Local vision, OCR, audio, document extraction, and small specialized models such as GLM-OCR, Gemma multimodal variants, and Ollama-OCR.
16%
Self-hosted AI applications
Ollama-powered private chat, research, workspace, automation, RAG, and agent stacks using tools such as Open WebUI, Vane, Dify, and Docker.
14%
Apple Silicon and MLX acceleration
Ollama's MLX backend on macOS, faster prefill/decode performance, NVFP4, and Apple unified-memory workflows.
8%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 57
- Median reposts
- 5
- Median replies
- 8
- Median views
- 5.4K
Posts with media make up 68% of this collection. Their median all-time score is 18.3, compared with 4.83 for text-only posts.
Format mix
- Announcement 36% · score 24.1
- Other 34% · score 17.1
- Tutorial 28% · score 5.44
- List 2% · score 66.0
Consensus and debate
Where creators agree, and where they do not
Shared view
Local control is the core appeal
Posts position Ollama as a route to local or offline inference, emphasizing user-controlled hardware and avoiding cloud API calls in the described setups.
Shared view
Model choice must follow hardware
Guidance repeatedly connects parameter size, quantization, context length, available RAM or VRAM, and task type. Smaller or quantized models are commonly presented as practical starting points.
Shared view
Compatibility expands coding workflows
Anthropic-compatible endpoints and editor integrations are presented as ways to use local open models with Claude Code, VS Code Copilot, and related coding-agent workflows.
Shared view
Specialist local models broaden use cases
The evidence includes Ollama-based tooling for document OCR, vision-driven extraction, and offline embeddings.
Open debate
Replacement versus hybrid deployment
Some posts frame local stacks as substitutes for paid or cloud-hosted services, while others recommend using local models for routine work and reserving stronger cloud models for difficult tasks.
Open debate
Performance claims require workload context
Apple Silicon MLX posts report faster inference and specific speed tests, while another practitioner reports that large local models can be slow on consumer hardware and trail cloud models.
Open debate
Ollama is not presented as the sole backend
Posts place Ollama alongside LM Studio, llama.cpp, vLLM, MLX, and routing layers, with different tools described for different efficiency or deployment needs.
What performs
Patterns behind standout posts
Media posts outperform text-only posts
Media-bearing posts have a median all-time score of 18.33, compared with 4.83 for text-only posts, indicating stronger observed performance for media in this dataset.
Offline setup drew the largest listed outlier
The Project N.O.M.A.D. local-first setup post is the highest listed engagement outlier. Local-model guidance and an Apple Silicon MLX update also appear among the listed outliers.
Announcements outperformed other multi-post formats
Announcements have a 24.06 median all-time score, above OTHER posts at 17.145 and tutorials at 5.44. The single LIST post has a higher 66.03 median, so it is not a comparable multi-post format.
Statistical standouts
- View standout post 1 Score 11430.7 · 703.43× median
- View standout post 2 Score 1802.4 · 110.92× median
- View standout post 3 Score 1485.8 · 91.43× median
- View standout post 4 Score 1365.9 · 84.06× median
- View standout post 5 Score 1240.2 · 76.32× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Vaishnavi
@_vmlops
2 posts
-
2. AshutoshShrivastava
@ai_for_success
2 posts
-
3. Charly Wargnier
@DataChaz
2 posts
-
4. Machina
@EXM7777
2 posts
-
5. GitHub Projects Community
@GithubProjects
2 posts
-
6. Hasan Toor
@hasantoxr
2 posts
Conversation is distributed across creators
The dataset contains 40 creators across 50 tweets, and the top-five placement share is 20%, indicating that the supplied conversation is not concentrated in a small set of voices.
Practical framing appears among prominent creators
Posts from DataChaz, EXM7777, and Hasan Toor connect Ollama to Anthropic-compatible coding workflows, local Gemma setup, and a self-hosted research stack, respectively.
Product-update posts cover platform changes
The supplied posts describe MLX acceleration for Apple Silicon and cloud hardware updates, connecting those updates to coding agents, assistants, and integrations.
Since the previous snapshot
What changed since Aug 12, 2026
- 78% of the selected posts remained.
- The creator count changed by 0.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Ollama tweets
Top Ollama tweets from 40 creators
Ranked 01–50
- 01
@alex_prompter ·
🚨 BREAKING: Someone just open-sourced a full offline survival computer with AI, Wikipedia, and maps built in. Project N.O.M.A.D. is an open-source offline survival computer. Self-contained. Zero internet required after install. Zero telemetry. Everything runs locally on your hardware. What it includes: → Full Wikipedia archives via Kiwix → Offline maps via OpenStreetMap → Local AI models via Ollama + Open WebUI → Calculators, reference tools, resource libraries → A management UI to control everything from a browser One curl command installs the entire system on any Debian-based machine. Runs headless as a server so any device on your local network can access it. Minimum specs to run the base system: dual-core processor, 4GB RAM, 5GB storage. To run local LLMs offline, you want 32GB RAM and an NVIDIA RTX 3060 or better. No accounts. No authentication by default. No cloud dependency. No phone-home behavior. Built to function when nothing else does. The grid, the cloud, the API you depend on. None of it is guaranteed. The people building local-first systems right now are the ones who won’t be asking for help when access disappears.
- 02
@gregisenberg ·
The takeaway from Fable 5 being BANNED by the government: GET GOOD AT LOCAL MODELS SO YOU HAVE 100% CONTROL. My entire weekend was going to be building my craziest ideas with Fable 5. That's now cancelled. So instead of building with Fable this weekend, I've decided I'll go deep on local models: 1. Start with the runtime. Download Ollama or LM Studio first. This is the thing that actually runs models on your machine. 2. Match the model to your hardware. A model's size is measured in billions of parameters (7B, 32B, 70B). Bigger is smarter but needs more memory. Rule of thumb: a 7B model runs on almost any laptop, a 32B needs a good Mac with 32GB+ RAM, a 70B needs serious hardware like a DGX Spark or a maxed-out Mac Studio. 3. Know which model for which job. Qwen 3 is the best all-around choice for most tasks. DeepSeek for reasoning and coding. Gemma 4 when you need something tiny that runs on a phone. Llama when you want the biggest community and the most fine-tunes. 4. Quantization. You can shrink a model to run on weaker hardware with barely any quality loss. Look for versions labeled Q4 or Q5. This is how a model that "needs" a server runs on your laptop. Learning this one concept changes everything. 5. Connect it to your agent. Point Hermes or your agent stack at a local model. 6. Context window is your real constraint locally. Cloud models give you huge context for free. Local models make you pay for it in memory. A bigger context window eats RAM fast. Keep your sessions tight and your prompts lean or your machine chokes. 7. Learn to give local models tools. A smaller local model with web search, file access, and code execution beats a giant model with none. The capability gap closes fast when you wire up the right tools. The model is the engine but the tools are the wheels. 8. Fine-tuning is more accessible than you think. You don't need this on day one, but know it exists. You can take an open model and train it on your own data so it gets good at your specific domain. I'll probably do a breakdown at some point on this @startupideaspod if people are into it. The lesson from this ban is basically don't build your entire workflow on something that can disappear with a single letter. Own part of your stack. Local models are insurance. It reminds me when people realized they don't own social media accounts. And then you saw people build email lists etc. I remember running a startup and my biggest traffic source was organic FB. All of a sudden, algo changed, and I lost 99% of my traffic. Same sorta moment (but bigger) for AI. This is a wake up call.
- 03
@ollama ·
Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework. This change unlocks much faster performance to accelerate demanding work on macOS: - Personal assistants like OpenClaw - Coding agents like Claude Code, OpenCode, or Codex
Watch video
- 04
@EXM7777 ·
here's how to run Gemma 4 locally in under 5 minutes: option 1 (phone): > download Google AI Edge Gallery from the Play Store > select Gemma 4 E2B or E4B > it downloads and runs entirely offline > no account, no API key, no internet needed option 2 (laptop): > install Ollama or LM Studio > pull gemma-4-27b (the MoE version, only 3.8B active params) > runs on a MacBook with 16GB RAM option 3 (developer): > open Google AI Studio > select Gemma 4 31B > use the function-calling API for agentic workflows > or deploy on Vertex AI for production the 26B MoE is the sweet spot for most people i think
- 05
@AlphaSignalAI ·
A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.
Watch video
- 06
- 07
@akshay_pachaar ·
this is huge. ollama is now compatible with the anthropic messages API. which means you can use claude code with open-source models. think about that for a second. the entire claude harness: - the agentic loops - the tool use - the coding workflows all powered by private LLMs running on your own machine.
- 08
@hasantoxr ·
A team in San Francisco killed Perplexity's $20/month subscription. It's called Vane. You get AI-powered search with cited sources, follow-up questions, image and video search, and focus modes for academic papers, Reddit, YouTube, and Wolfram Alpha, running entirely on your own machine. Here's how it works. Vane is an open-source clone of Perplexity built on top of SearxNG which is a meta-search engine that pulls results from Google, Bing, DuckDuckGo, Brave and 70+ other sources without tracking the user. You plug in any LLM you want including OpenAI, Anthropic, Groq or local models through Ollama and it answers your questions with real citations pulled from the live web in real time. The entire stack can run 100% locally with Llama 3 and SearxNG on your own hardware which means zero API calls going out and zero data ever leaving your machine. → No $20/month Pro subscription holding the good models hostage → No query limits cutting you off mid-research → No tracking and no profile being built from your searches → Local mode with Ollama supporting Llama, Mistral, Qwen and anything else you throw at it → Focus modes that narrow the search to Academic papers, YouTube, Reddit, Wolfram Alpha or Writing → Image and video search built directly into the interface → Copilot mode that breaks one question into multi-step research and synthesizes the findings Perplexity charges $20 a month for Pro and trains its ranking algorithm on every query you send them. Their entire business model assumes you would never spend an evening with a Docker compose file and a local LLM. Vane runs in one container and SearxNG runs in another and the whole thing points at a Llama 3 model running on your laptop with no internet account involved anywhere in the chain. MIT License. 100% Opensource. https://t.co/rg17qIjsH3
- 09
@fahdmirza ·
Gemma 4 + OpenClaw + Ollama + Discord — Full Local AI Setup for Free 🔥 Google just dropped Gemma 4 and we wired it directly into Discord 🔹 Gemma 4 31B pulled via Ollama — completely local 🔹 Fresh OpenClaw install from scratch 🔹 Full Discord bot setup — Developer Portal, intents, OAuth2, permissions 🔹 OpenClaw + Discord pairing walkthrough 🔹 Chat with Gemma 4 directly from your Discord server 🔹 DuckDuckGo web search enabled — no API key needed Watch the full setup below 👇
- 10
- 11
@ollama ·
Ollama's cloud is updated to use NVIDIA's latest data center hardware: B300 for Kimi K2.5 and GLM-5 models. This significantly improves the model performance with faster throughput and lower latency while maintaining reliable tool calls for integrations. All this works with Ollama's integrations via Ollama's launch command and over 45,000 custom integrations from GitHub.
- 12
@EXM7777 ·
how to set up OpenCode (the privacy-first Claude Code alternative) in 3 minutes: > install: npm i -g opencode-ai > configure your preferred model provider > cd into your project directory > run: opencode it reads your codebase locally, builds context on your machine, and only sends the specific prompt + relevant code to the LLM this is the best configuration for privacy-sensitive work: > use Ollama with Gemma 4 > zero data leaves your machine > you get agentic coding with file editing, terminal access, and multi-step reasoning > completely free yet to match frontier models, but it's kinda crazy we can run this type of workflow on a mid laptop
- 13
@GithubProjects ·
Odysseus is a self-hosted AI workspace that runs on your own hardware with local-first, privacy-first data handling. - Chat with local models or APIs via vLLM, Ollama, or OpenAI - Agent with MCP, web, files, shell, and persistent memory - Cookbook scans hardware and recommends VRAM-aware models - Deep Research gathers and synthesizes sources into visual reports Explore it here: https://t.co/bHEWmceUB9
- 14
@KanikaBK ·
I found 5 AI agent GitHub repos that most developers have never heard of. COMBINED STARS: 750,000+. ALL OPEN SOURCE. ALL FREE. Here is exactly what each one does. 1. n8n: https://t.co/y5MH1YnDFm ↳ 180,000+ stars and still climbing ↳ Open source alternative to Zapier but built for serious technical teams ↳ 400+ integrations, visual workflow builder, and native AI agent nodes ↳ Self-host it and get unlimited automations for free with zero per-task fees 2. Dify: https://t.co/Sww2R8KVT5 ↳ 136,000+ stars ↳ Build, deploy, and manage AI applications without drowning in boilerplate code ↳ Comes with RAG pipelines, prompt orchestration, model management, and usage monitoring built in ↳ Supports OpenAI, Anthropic, Ollama, and 100+ other model providers out of the box 3. Langflow: https://t.co/3cBWhbnHSv ↳ 146,000+ stars ↳ Drag and drop visual builder for AI agents and RAG workflows ↳ Shortens the path from idea to working AI pipeline without writing extensive code ↳ Used by data scientists and engineers who want to prototype and ship fast 4. Open WebUI: https://t.co/ISqMHwCnyd ↳ 124,000+ stars and 282 million Docker downloads ↳ Self-hosted ChatGPT-style interface that runs completely offline ↳ Connects to Ollama and any OpenAI-compatible API with one pip command ↳ Built-in RAG, voice features, custom agent builder, and enterprise SSO 5. Ollama: https://t.co/nnQaDjLa1T ↳ 165,000+ stars ↳ Run Llama, Mistral, Gemma, and DeepSeek entirely on your own hardware ↳ No data sent to external servers, no API fees, no cloud dependency ↳ The backbone of almost every serious self-hosted AI stack running today Most developers are paying per token for things they could be running locally for free. These five repos are why that excuse is running out.
- 15
@witcheer ·
I run ollama on a Mac Mini for local compression. every message my agent sends passes through a local qwen model to summarise context before it overflows. speed matters because slow compression means slow responses across every cron job. ollama shipped MLX backend for Apple Silicon. the numbers on M5: → prefill: 1,810 tok/s (was 1,154). 57% faster → decode: 112 tok/s (was 58). 93% faster that's not a marginal improvement. my compression pipeline got nearly 2x faster with a software update. three things that matter beyond the speed numbers: 1/ the cache got smarter. ollama now reuses cache across conversations and stores snapshots at intelligent points in the prompt. for agent workloads with shared system prompts, (which is exactly how OpenClaw works) this means less re-processing on every call. 2/ NVFP4 quantisation. NVIDIA's 4-bit format, now running locally on Apple Silicon. same quantisation format used in production inference. your local model and the cloud model now speak the same precision language. 3/ the model they ship with it: qwen3.5-35B-A3B, a 35B mixture-of-experts with only 3B active parameters. tuned for coding tasks. needs 32GB+ unified memory.
- 16
- 17
@andrewchen ·
playing around with local AI models after I recently built out my home lab (DGX spark, mac mini, 5090 eGPU, strix halo framework, jet KVM etc). Running both Openclaw and Hermes Agent now. It’s super fun, def recommend! Lets you geek out, learn about AI, and also buy lots of gadgets lol a few observations: - it’s great for learning about AI. Now I actually care and will try out all the new models as they come out - Qwen 3.6, Gemma 4, etc. When there’s new tech like TurboQuant and DFlash, you can run them on your machine and see how it changes the performance profile - the software stack is interesting. You can use ollama/LM studio to just dabble, but over time I have things set up with LiteLLM (as a local router for LLM queries, depending on their complexity) going to VLLM. I have a faster model (35B MoE) and then a better model (122B) depending on what I’m using it for - the “big” local models (120B+ parameter) are slow unless you have a souped up GPU card. And not as good as the cloud LLMs. So as you tune your setup for maxing out tokens/s to make it as usable and responsive, you get a much better sense for all the tradeoffs - context window, KV cache, mem usage, mem bandwidth, parameter size, TTFT, etc - for those (like me) coming from SOTA cloud LLMs, you can’t help but compare. The open weight models are all about a year behind, but even then, as a consumer, you are generally running much smaller versions of the best local models. You probably won’t use anything bigger than a ~120B parameter model (GPT OSS 120B or Qwen 3.6 122B). Local AI models running on consumer hardware have 1/100th the size, are much slower (often 30-50 tok/s versus 100+ to be usable) - but because it’s been ~1year behind, it seems remarkable to think that we might be able to run Opus level local models in 2027. The latest open weight models are already pretty usable (just look at Qwen 3.6 27B dense) but its remarkable that it’ll keep improving - the hardware side is interesting. I started out with a Mac Mini, then a Nvidia DGX Spark. I also have a gaming rig. It turns out that the Mac hardware stack (particularly Mac Studios) are really good since they have pretty high bandwidth and large amounts of unified memory so you can run big models. (BUT GOOD LUCK GETTING A MAC STUDIO!). Shortages like crazy, and memory size cuts left and right. GPU cards are very fast, but only run much smaller models (24GB and 32GB are the popular consumer sizes for graphics cards), plus you have to put them in a big PC box. I got a 5090 eGPU but lots of issues with it :(. The new GB10/DGX Spark family of devices have big memory but relatively low memory bandwidth (so not the fastest tok/s) but you get CUDA and the whole ecosystem there - the biggest use case I’ve found with my local AI setup has been simple: lots of summarization and analysis. I’ve dumped all my personal emails and blog posts and google data and created detailed month-by-month markdown files that can then be queries. Every article I bookmark or every YouTube channel I subscribe to is summarized. for me the sweetspot has been low-ish priority, asynch, and where the problem doesn’t require SOTA You could argue that this is a lot of effort and $ for something that could probably be covered by my monthly GPT/Claude subscription. And that’s true! But the learning is the point :) so what’s a good way to start? I think you start with whatever you have. Ideally a nice Mac M5 laptop or a gaming PC that already has a good GPU. Just set it up so it stays on, and then point some set of Openclaw jobs at it. Or if you want to invest in a new piece of hardware, the DGX Spark or Strix Halo systems are nice to be able to try out bigger models, or you can go down the rabbit hole setting up racks with GPUs etc. Either way, super fun- highly recommend
- 18
@ai_for_success ·
Google DeepMind has released Gemma 4 12B, a unified encoder free multimodal model built for running agentic AI locally on laptops. 🔥 - 12B parameter model that runs on laptops with 16GB memory - Encoder free architecture for native image and audio processing - Performance close to the larger 26B MoE model - Native audio support with raw audio token processing - Multi Token Prediction for lower latency - Open sourced under Apache 2.0 - You can try here LM Studio, Ollama, Google AI Edge Gallery App, the Google AI Edge Eloquent app and the LiteRT-LM CLI - New Gemma Skills Repository for agentic workflows
- 19
@alex_verem ·
Found an open source tool that watches your screen and pings you when something happens. It's called Observer. You build tiny AI agents that monitor your screen, camera, or mic with a local model, then react. Your training run crashes, you get a Telegram message. A dashboard error rings your phone with a text-to-speech readout. Sensors: screen, OCR, camera, mic, clipboard, meeting audio. Actions: email, Discord, Telegram, WhatsApp, phone calls, screen recording. The models run through Ollama or llama.cpp. No cloud API sees your screen. Setup: system prompt + sensor + a few lines of JS. One agent takes minutes. 1.6k stars. Point an observer at your screen and go do something else.
- 20
@tonysimons_ ·
Hermes Agent just got a serious speed injection. First-turn startup latency was cut by ~80%. Cold submit → request dispatch: 4.3s before 0.9s after The fix? @Teknium tracked the actual pre-request stalls and cut them out: 🔹 Discord capability detection moved off the blocking path 🔹 pointless Ollama probes skipped for known non-Ollama providers 🔹 Python env probing warmed off-thread 🔹 MCP imports skipped when there are no MCP tools 🔹 CLI pre-imports while you’re still typing Hermes keeps getting sharper. PR: https://t.co/6FYpEGQulR
- 21
@hasantoxr ·
Anthropic Fable 5 has been banned by the government. Learn to use local models so you have 100% control. Instead of arguing about why they banned it, I built a full guide on running AI locally so nobody can ever take it from you. Here's everything you need to know: 1. Pick your runtime first. Think of this like installing a video game launcher before you can play any games. Ollama and LM Studio are the two launchers for AI. Download one. That's step one. 2. Understand model size. The number next to a model like 7B or 32B just means how many things it learned. Bigger number means smarter but needs more memory. A 7B runs on any laptop you already own. Start there. 3. Pick the right model for the job. Qwen 3 for everyday tasks and writing. DeepSeek for math and coding problems. Gemma 3 when your computer is slow. Llama when you want the most help from the internet because millions of people use it. 4. Learn quantization. This sounds scary but it's just shrinking. Like zipping a file. A huge model gets compressed so it fits on your laptop with almost no quality loss. Look for Q4 or Q5 in the model name. That's the compressed version. Download that one. 5. Add tools to your model. A small model with Google search and file access beats a big model with nothing. Think of tools like giving the AI hands. Without them it can only think. With them it can actually do things. 6. Watch your context window. Cloud AI has unlimited memory per conversation. Local AI does not. The longer your chat gets the more your laptop slows down. Keep conversations short. Start a new chat when things feel slow. You never needed anyone's permission to use AI. The ban only hurts people who never learned to run it themselves.
- 22
@itsharmanjot ·
GitHub is shutting down its entire AI playground on July 30, 2026. Playground. Model catalog. Inference API. BYOK. All of it. Gone for every customer including people with active usage right now. What GitHub Models was: Free access to Llama 3.1, GPT-4o, Mistral, Cohere directly inside GitHub. No separate account. No setup. Zero friction prototyping since 2024. Where GitHub is pointing you instead: Azure AI Foundry. Microsoft's paid platform. If you have pipelines calling the inference API they break July 30. Find them now while access still works. GitHub scheduled brownouts July 16 and July 23 as live tests. Use them. What to actually use instead: For prototyping locally → Ollama. One command, any open-weight model, runs on your hardware, no cost, no rate limits, no shutdown risk. For a ChatGPT-style interface on top → Open WebUI. One Docker command. For a desktop GUI with no terminal → LM Studio. For API-dependent production workflows → OpenRouter. One endpoint, dozens of models, provider-portable. The pattern worth naming: Free tool lowers the barrier. Grows the user base. Gets retired toward the paid platform. This is not a GitHub problem specifically. It is the standard enterprise playbook for developer tools. Local models running on your own hardware cannot be retired by someone else's changelog post. 21 days left. If anything in your stack touches GitHub Models, the time to find out is now.
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- 23
@VaibhavSisinty ·
I've been saying this for a while now. The future isn't one massive model sitting in the cloud doing everything for you. It's tiny specialist models running on your device. Doing 80% of tasks locally. A small router model deciding which model handles what. And only pinging the cloud when the task genuinely needs a bigger brain. People called this wishful thinking. Then China dropped GLM-OCR this week. 0.9 billion parameters. That's nothing. Practically runs on a potato. And it just became the #1 document reading model in the world. Better than Gemini. Better than models literally 100x its size. The trick is simple. It doesn't read a document top to bottom like most models. It breaks the page into regions first, then reads all of them at the same time. Predicts multiple tokens per step. Small model. Parallel processing. Insane speed. Tables, handwriting, math equations, messy layouts. It handles all of it. Runs locally through Ollama. Open source. Free. This is exactly the kind of model that fits into the future I'm describing. You don't need GPT-5 to read a receipt. You need a tiny model that does one job really well, sitting on your phone, costing you nothing. One model to rule them all is dead. Swarms of small specialists is what's coming.
Watch video
- 24
- 25
@Cyb3rMaddy ·
Been messing around with local, uncensored LLMs... Ollama runs open-source LLMs locally. It’s handy for security research, private workflows, and anything you don’t want leaving your machine — even works without internet. No cloud calls. No sending prompts somewhere you can’t see. Which you can scale on an enterprise level if needed, or just have your own custom private AI. You can even mod these with your own system prompts. Messing around with dolphin-llama3:8b for red teaming. As you can see below, totally unrestricted. At least it gives a warning...? ☠️
- 26
@ayushagarwal ·
contextmcp v0.5.0 just shipped. contextmcp is our open-source MCP server that indexes your documentation and serves it as context to AI agents. point it at your docs repo, it chunks, embeds, and gives your agent the right documentation when it needs it. what's new: → Ollama support. fully offline, no API key, zero cost local embeddings. also added Cohere and Voyage AI as providers → GitLab source. index docs from gitlab or self-hosted instances. not just GitHub anymore → validate and doctor commands. catch misconfigs before you waste time on a full reindex all backward compatible. 50+ stars. @dodopayments
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- 27
@DivyanshT91162 ·
Everyone keeps asking: "Can my PC run this LLM?" Now there's finally a tool that answers with actual data. llmfit. Instead of guessing, it scans your hardware and ranks hundreds of LLMs based on: • Memory fit • Estimated speed • Model quality • Context length It also supports: → Multi-GPU setups → MoE models → Dynamic quantization → Ollama, llama.cpp, MLX, LM Studio & Docker Model Runner → Interactive TUI + CLI The coolest part? You can benchmark real token/s on your own machine and contribute those measurements back to the project. That means recommendations get more accurate over time—and users with the same hardware can see verified performance before running a single benchmark. No more downloading 40GB models just to discover they crawl at 2 tok/s. 100% Open Source. License: MIT. Worth bookmarking if you're running local AI. REPO👇
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@GithubProjects ·
Chat UI is a SvelteKit chat interface that works with any OpenAI-compatible API, powering HuggingChat at https://t.co/3Q2b7hKvgg. - Connects to any OpenAI-compatible endpoint via OPENAI_BASE_URL and /models - Supports llama.cpp, Ollama, OpenRouter, and the Hugging Face Inference Providers router - Persists chat history, users, and settings in MongoDB with an embedded fallback - Runs as a local dev server with npm install and npm run dev Explore it here: https://t.co/dvzEIES28e
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@_vmlops ·
OLLAMA-OCR TURNS YOUR SCANNED DOCS INTO CLEAN MARKDOWN built on top of ollama's local vision models, no cloud APIs, no OCR subscriptions → swap between llava, llama 3.2 vision, granite3.2-vision, moondream, minicpm-v depending on speed vs accuracy needs → output as markdown, plain text, JSON, tables, or key-value pairs → batch process entire folders in parallel with progress tracking built in → built-in image preprocessing before it even hits the model → streamlit web app included if you don't want to touch code pip install and you're extracting text in minutes. 2.3k stars, actively maintained your documents never leave your machine.
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@_vmlops ·
LOCAL AI MODELS INSIDE COPILOT CHAT This vs code extension just changed the game run deepseek, llama, qwen locally via ollama all through github copilot chat no api keys...no cloud...no switching tabs inline completions, tool & mcp support, vision, model switching without ever leaving the editor copilot chat just became a local ai playground
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@aaliya_va ·
Stop downloading LLMs; your machine was never going to run. llmfit scans your hardware and tells you exactly which models will run. It scans your RAM, CPU, GPU, and VRAM first. Then it scores every model across four dimensions: 1. Quality, based on parameter count and quantization 2. Speed, estimating tokens per second for your exact backend 3. Fit, matching memory use to your hardware 4. Context window support for your use case Each model gets a label: Perfect, Good, Marginal, or Too Tight. It picks the best quantization automatically, stepping down until something fits. Covers hundreds of models from Meta, Mistral, Qwen, and DeepSeek. Works with Ollama, llama.cpp, MLX, and LM Studio out of the box. Open-source. Link in comments ↓
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@ai_for_success ·
Running the Muse Glimmer MLX version locally via Ollama, and I’m getting around 45 to 50 t/s. I tested it with my Hermes Agent setup as well, and it’s good. Been running it since yesterday, and so far, so good. Pretty impressive for its size. Obviously, you can’t compare it to much larger models.
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@NainsiDwiv50980 ·
Your agentic AI product can earn its first dollar before it generates its first model API bill. Not a toy chatbot. A real system that retrieves knowledge, makes decisions, calls tools, takes actions, retains state, and traces what happened, running on a stack that costs exactly $0 to start. Here's the full pipeline: → Interface: Next.js or Streamlit takes the request → Orchestration: LangGraph or CrewAI decides whether to answer, retrieve, call a tool, ask for approval, retry, or stop → Knowledge: LlamaIndex pulls context from ChromaDB or Qdrant, running locally → Reasoning: Ollama runs an open model (Gemma, Llama 3.3 70B, Mistral) on your own hardware, zero API bill → Action: MCP connects the agent to files, databases, GitHub, Slack, browsers, this is the step where a chatbot becomes a worker → State: SQLite or DuckDB stores conversations, checkpoints, outputs → Observability: Langfuse or Phoenix traces every prompt, decision, tool call, and failure → Deployment: Docker packages it, inference runs on hardware you already own, only the lightweight interface sits on a free tier Total software and API spend to get this running: $0. But here's the part that actually matters, the free tools aren't the advantage. Every single piece here will eventually get replaced by something faster or cheaper. Ollama becomes a hosted API. SQLite becomes a production database. Streamlit becomes a custom app. The advantage that survives every swap is knowing: → Where reasoning should stop and deterministic code should take over → When RAG actually improves an answer versus just adding latency → Which actions genuinely need human approval → What has to be traced before your first production failure, not after → How to isolate every layer behind a replaceable interface Build the cheap version first. Learn exactly where users find real value. Then spend money only where it creates leverage, not before. If you had $500 a month to put into scaling this stack, which layer gets it first?
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@RoundtableSpace ·
Ornif 1.0 is a free local model running through Ollama that's reportedly beating models 10x its size. It writes its own plan before coding, then grades itself on both the strategy and the output.
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@Axel_bitblaze69 ·
local AI is now useful enough to keep at home imp.. a small machine sitting on your desk can now handle a large part of your everyday AI work for roughly a few dollars in electricity each month, depending on what you run and how often. things like: - drafting - summarising - basic coding help - classifying information - reading your own files - running repetitive automations all without paying for every request or sending the work to a cloud model. the goal isn’t to replace Claude or ChatGPT completely. the better setup is probably: local AI for the repetitive, private, high-volume work one strong cloud model for the difficult stuff first, prove the workflow tonight for free don’t buy anything yet. install Ollama, then run a small model: ollama run llama3.2:3b that’s it. you’re talking to a model running on your own computer. many modern laptops with around 8GB of memory can run a small quantized model, although the speed and usable context will depend on the machine. Ollama supports macOS, Windows and Linux. and if you’d rather use a proper chat window than a terminal, install Open WebUI on top. it connects to Ollama and gives you something much closer to a private ChatGPT interface running on localhost. use it for a weekend. not because a free 3B model performs like a $2,000 setup. it obviously doesn’t. you’re testing something more basic: does running AI locally fit the way you work? try it on your documents, notes, repetitive writing and simple automations. if those workflows feel useful, then better hardware gives you stronger models, longer context and faster output. one rule before buying anything buy memory first, speed second. model size is the first hard limit. if the model doesn’t fit into available memory, it either won’t load properly or will spill into slower system memory and become painful to use. speed matters after that. memory decides what you can run in the first place. a rough ladder: - 8GB → small 3B–7B quantized models - 24GB → 14B models comfortably, some 32B models with compromises - 128GB → 70B-class quantized models and much longer contexts these aren’t hard laws. quantization, context length and how much memory the operating system already uses can change the result. unified-memory machines are interesting because the CPU and GPU draw from one shared memory pool instead of the GPU being limited to a separate chunk of VRAM. the hardware ladder around $249 → Jetson Orin Nano Super, 8GB the cheapest dedicated entry. tiny, efficient and capable of running smaller local models, although it’s more of a developer or edge-AI machine than a normal desktop. NVIDIA currently lists it at $249. from $599 → Mac mini M4, 16–32GB unified memory probably the easiest quiet, always-on local AI box to live with. the M4 model starts with 16GB and can be configured to 24GB or 32GB. used RTX 3090 → 24GB VRAM still an obvious option when you want a lot of NVIDIA VRAM without buying a current flagship card. but remember, the GPU isn’t the computer. you still need the motherboard, CPU, RAM, storage, case and power supply around it. Used pricing will depend heavily on your market. The RTX 3090 itself has 24GB of GDDR6X memory. around $2,000 → Ryzen AI Max+ 395 with 128GB unified memory this is where local AI starts becoming serious. AMD supports configurations with up to 128GB of unified memory, with a large share available to the GPU, and Framework introduced its 128GB desktop configuration at $1,999. $4,000+ → high-end Mac Studio and DGX Spark territory this only starts making sense when you’re running large models constantly, fine-tuning, building AI products or already spending serious money on cloud GPUs. DGX Spark has 128GB of unified memory, and NVIDIA raised its Founders Edition MSRP to $4,699 in February 2026.
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@Shruti_0810 ·
The most expensive part of this AI setup isn't the hardware. It's... nothing. A Raspberry Pi 5 with 16GB RAM and a 512GB NVMe is powering: • Local LLMs with Ollama • Claude Code via localhost • Bluetooth analysis • Wi-Fi security testing • Packet capture All from a device that fits in a shoulder bag. Instead of sending prompts to the cloud, everything runs locally. That means: → No API bills → No internet required for inference → Sensitive data stays on the device → One machine handles both AI and security workflows The idea of carrying an AI coding assistant and a portable security lab together would've sounded unrealistic not long ago. Now it's a Raspberry Pi project anyone can build.
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@SaiyamPathak ·
Ollama just replaced launched 0.19 with Apple's MLX framework on Apple Silicon. The result? ~2x faster inference as per there test on M5 I tested it on my M1 Max the difference is real. New video breaking down: → What MLX is and why it's faster → UMA explained → Prefill vs Decode → NVFP4 quantization → Real benchmarks (MLX vs llama.cpp) Watch full video 👇
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@TheCraigHewitt ·
Getting local AI models set up is easier than you think. In this video I compare Ollama to LMStudio as well as explore the newest open weight models like Gemma 4 and Qwen 3.5. Local models are just getting really good, and I think can replace 50% of what you're doing with frontier models like Opus 4.6 and GPT 5.4 And they're just getting better, getting smaller, and easier to run.
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@RoundtableSpace ·
Configuring Codex CLI to use OpenRouter or local Ollama endpoints provides a zero-cost, rate-limit-free alternative for autonomous coding.
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@JulianGoldieSEO ·
𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗹𝗮𝗽𝘁𝗼𝗽 𝗮𝗻𝗱 𝗿𝗮𝗻𝗸𝗲𝗱 𝗻𝘂𝗺𝗯𝗲𝗿 𝟯 𝗶𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗼𝗻 𝗮𝗻 𝗼𝗽𝗲𝗻 𝗺𝗼𝗱𝗲𝗹 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱. No subscriptions. No data leaving your machine. No internet needed once it's set up. Here's which model to pick and how to run it: → Standard laptop: ollama run gemma4:e4b (9.6GB, runs at 4B during inference) → Higher-end workstation: ollama run gemma4:26b (18GB, fast and high quality) → Maximum quality: ollama run gemma4:31b (20GB, ranked #3 globally) Settings most people skip that actually matter: → Temperature 1.0, top_p 0.95, top_k 64. Google recommends these for all Gemma 4 models. Use them. → Enable thinking mode for complex tasks. Include the think token at the start of your system prompt. Turn it off for simple fast answers. → Put your image before your text in every prompt. Better performance according to the official docs. → For image tasks: use lower token budgets for classification, higher budgets for OCR and document reading. → Don't feed thinking output back into your conversation history. Only the final response. Make sure Ollama is on version 0.20 or higher before pulling. Gemma 4 requires it. Apache 2.0 license. Commercial use. No restrictions.
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@DomJoLuna ·
Everybody's panicking about Anthropic cutting off OpenClaw today. A lot of people are switching to OpenAI as their default model, and to be frank, that's a downgrade you don't need to make. Here's what's actually happening…Anthropic is separating subscription limits from third-party tool usage. Your Claude subscription still works. Your Claude account still works. You just need to enable "extra usage" in your account settings (it's a pay-as-you-go option billed separately). They're even giving you a one-time credit equal to your plan cost and offering up to 30% off on pre-purchased usage bundles. So before you rip out the brain that actually feels like a thinking partner and replace it with something that reads like a corporate memo, just turn on extra usage. It takes 30 seconds. But here's the bigger play that nobody's talking about (cause AI releases move at light speed nowadays) Google dropped Gemma 4 two days ago under Apache 2.0. Fully open, fully commercial, zero restrictions. The 26B Mixture-of-Experts model is currently ranked #3 open model in the world, and it only activates 3.8 billion parameters during inference. That means it runs on a Mac Mini. Read that again. A top-3 open model running locally on a $600 machine. We're deploying it across our compute cluster today. Here’s the setup: → Ollama + Gemma 4 26B MoE for all routine work (heartbeats, task execution, research, monitoring) → TurboQuant KV cache compression to keep memory tight on 16GB machines → Claude Opus stays as the executive brain for complex strategy and conversation → Projected savings: ~$2k+/month by moving operational workload off API If you're running OpenClaw on a Mac Mini or any Apple Silicon machine, you can do the same thing right now: ollama pull gemma4:26b Set it as your primary model in OpenClaw, keep Claude as your fallback for the conversations that matter, and your monthly API bill drops to almost nothing for routine work. The best part? Gemma 4 has native function calling, 256K context, and system instruction support built in. It's not a toy, it handles OpenClaw's tool chain natively. Don't downgrade your AI partner because Anthropic changed a billing policy. There are better moves on the board.
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@JeremyCMorgan ·
If your last local-LLM setup was early 2025, the viable model list moved. This refresh covers Ollama, llama.cpp, and VRAM sizing, with Qwen 2.5 Coder 32B the standout coding pick at 24GB, scoring ahead of GPT-4o on HumanEval per the post. Recalibrate before buying a box. https://t.co/586xnyLAtR
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@JulianGoldieSEO ·
𝗥𝘂𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 𝗶𝗻 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝘂𝘀𝗶𝗻𝗴 𝗢𝗹𝗹𝗮𝗺𝗮. Ranked number 3 in the world among open models. Runs on your laptop. No cloud. No data leaving your machine. Here's exactly which model to pick: → E4B (9.6GB): Start here if you're on a typical laptop. Run: ollama run gemma4:e4b → 26B MoE (18GB): Only activates 4B parameters during inference. Fast with high quality. Run: ollama run gemma4:26b → 31B dense (20GB): Maximum quality. Number 3 globally. Run: ollama run gemma4:31b Best practices most people skip: → Set temperature 1.0, top_p 0.95, top_k 64. Google recommends these across all use cases. → Turn thinking mode ON for math, coding, and analysis. Turn it OFF for quick summaries. → When sending an image with a question put the image first then your text. Better performance. → Don't include the model's thinking output in your conversation history. Only final responses. Coding benchmark jumped from 110 ELO on Gemma 3 to 2,150 on the 31B. That's a generational gap in one release. Apache 2.0 license. Commercial use, fine-tuning, redistribution. No hidden restrictions. Install Ollama at https://t.co/493GbXWz04. Make sure you're on version 0.20 or higher for Gemma 4 support.
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