Hardware fit and inference benchmarks
RAM/VRAM, Apple Silicon, RTX GPUs, DGX Spark, Framework and AMD systems, plus token-per-second, context, KV-cache, and latency trade-offs.
38.2%
Best tweets about LM Studio
34 Best Tweets About LM Studio (2026)
Browse the best tweets about LM Studio, featuring local LLM setup, model downloads, hardware performance, local servers, APIs, and private AI workflows.
Specific LM Studio setup, local inference, model compatibility, hardware performance, APIs, troubleshooting, and releases.
- Creators
- 33
- Updated
Original Xholic analysis
What 34 top LM Studio posts reveal
LM Studio discussion concentrates on local inference, hardware-aware model selection, and agent or API integrations. Posts are largely supportive, while also describing memory, latency, vision, context, and tool-use trade-offs relative to cloud services.
- Dominant tone
- Positive
- Median score
- 9.84
- Leading format
- Announcement
- Recent posts
- 35.3%
73.5% of posts
All-time engagement
41.2% of posts
Published in 90 days
Conversation map
The themes creators return to
Agents, tool use, and coding workflows
Connecting LM Studio models to Hermes Agent, Codex, Claude Code proxies, MCP-style integrations, skills, sandboxing, and local agent loops.
35.3%
Model choice, sizing, and quantization
Choosing Qwen, Gemma, Llama, DeepSeek, and other models by task; fitting parameter sizes and Q4/Q5-style quantizations to available memory.
35.3%
Local setup, offline use, and data control
Installing LM Studio to run models privately on personal hardware, without cloud APIs, subscriptions, or internet access.
29.4%
Troubleshooting and local-model limitations
Vision failures, oversized models, slow responses, context overload, tool-call reliability, and quality gaps versus frontier cloud models.
23.5%
LM Studio as a local server and API
Using LM Studio’s OpenAI-compatible localhost API or server mode with apps such as Raycast, AnythingLLM, coding tools, and custom clients.
20.6%
Model releases and runtime compatibility
Availability and behavior of new open models and formats across LM Studio, including GGUF, MLX, MoE, multimodal models, and llama.cpp backends.
17.6%
Remote inference with LM Link
Using LM Link, Tailscale, and a desktop inference machine to serve local models securely to laptops and other networked devices.
11.8%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 29
- Median reposts
- 3
- Median replies
- 4
- Median views
- 2.1K
Posts with media make up 70.6% of this collection. Their median all-time score is 7.49, compared with 36.5 for text-only posts.
Format mix
- Announcement 41.2% · score 16.1
- Tutorial 17.6% · score 15.6
- Case Study 14.7% · score 3.07
- Opinion 14.7% · score 6.13
Consensus and debate
Where creators agree, and where they do not
Shared view
Local control is a recurring appeal
Posts describe LM Studio as a way to run models on personal hardware, including offline or no-cloud-API-key setups and greater control over local workflows.
Shared view
Hardware should inform model choice
Guides pair parameter size and quantization with available RAM or VRAM, and recommend smaller models or Q4/Q5-style variants when memory is constrained.
Shared view
LM Studio is used beyond chat
Examples connect LM Studio-hosted models with agents, coding tools, desktop applications, Raycast, and AnythingLLM through local serving or localhost integrations.
Open debate
Local capability versus cloud quality
Some posts present local models as useful for focused workflows, while others report that larger local models can be slower or less capable than cloud models, particularly for demanding agentic or tool-use work.
Open debate
Accessible setup versus operational limits
Setup posts characterize local use as approachable, but firsthand reports also describe vision issues, a 31B model failing on one laptop, and materially slower performance on harder reasoning tasks on laptop hardware.
What performs
Patterns behind standout posts
Guides and setup announcements led the strongest outliers
The two highest-scoring outliers were the local-model workflow guide and the Gemma local-setup guide, followed by the Hermes Agent local-setup announcement. Format medians were also higher for announcements (16.12) and tutorials (15.63) than for case studies (3.071) and opinions (6.135).
Remote inference was a high-scoring niche
The remote-inference-with-LM-Link theme had the highest theme median all-time score, 29.4, while covering four tweets.
Media was prevalent, while text-only posts had a higher median score
Media appeared in 24 of 34 tweets (70.6%). The supplied analytics report a 7.49 median all-time score for media posts versus 36.55 for text-only posts.
Statistical standouts
- View standout post 1 Score 1802.4 · 183.17× median
- View standout post 2 Score 1365.9 · 138.81× median
- View standout post 3 Score 298.9 · 30.38× median
- View standout post 4 Score 118.3 · 12.02× median
- View standout post 5 Score 95.7 · 9.72× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 17.6% of the selected posts.
-
1. Julian Goldie SEO
@JulianGoldieSEO
2 posts
-
2. Vaishnavi
@_vmlops
1 post
-
3. AshutoshShrivastava
@ai_for_success
1 post
-
4. AlphaSignal AI
@AlphaSignalAI
1 post
-
5. andrew chen
@andrewchen
1 post
-
6. Beto
@betomoedano
1 post
Participation was broadly distributed
The sample contains 33 creators across 34 tweets, and the top five creator placements account for 17.6% of placements, so no single creator accounts for a large share of this sample.
Several posts offer concrete implementation details
Examples include hardware inventories and trade-offs, measured token speeds, runtime and quantization comparisons, and reported laptop limitations rather than only general advocacy for local AI.
Since the previous snapshot
What changed since Aug 13, 2026
- 64.7% of the selected posts remained.
- The creator count changed by +5.
- 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 34-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 LM Studio tweets
Top LM Studio tweets from 33 creators
Ranked 01–34
- 01
@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.
- 02
@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
- 03
@fahdmirza ·
💥 Hermes Agent is now running locally with Qwen3.5 + LM Studio ⚕ ♠ and this is the cleanest local self-improving agent setup I've done yet 🚀 🔹 Zero cloud, zero API keys — everything runs on your own hardware 🔹 LM Studio makes model loading and serving dead simple 🔹 Hermes Agent creates skills from experience and remembers you across sessions 🔹 Closed learning loop in action — fresh session, still knows who you are 🔹 Full setup from scratch — AppImage install, model download, agent config, all of it 🔥 Full setup guide below 👇
- 04
@NVIDIARTXSpark ·
Run the latest @Alibaba_Qwen 3.5 models at blazing speeds on RTX GPUs & DGX Spark using @UnslothAI's GGUF quantizations. 💬 Chat instantly in @LMStudio 🤖 Drive local coding agentic workflows with Codex & Claude Code (via llama.cpp @ggerganov & @Ollama) ⚙️ Fine-tune efficiently via Unsloth Guide 👉 https://t.co/fWtiiz3XyS
- 05
@Forgework_ ·
Join us in setting up a fully local Hermes Agent using Qwen3.5 on the extremely powerful Framework Desktop! I go over LM studio settings, Tailscale, Hermes Agent setup, and how to sandbox the agent in a raspberry pi. @lmstudio @Alibaba_Qwen @NousResearch @FrameworkPuter
Watch video
- 06
- 07
@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
- 08
- 09
@FrameworkPuter ·
One of the coolest uses of @lmstudio and @AIatAMD is using LM Link to have a Framework Desktop be a remote inference server for a laptop, including something little like Framework Laptop 12. It feels native on the laptop, but gets the desktop’s performance.
Watch video
- 10
@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.
- 11
@ai_for_success ·
Running Gemma 4 12B locally at 28T/s. Facing issues with vision in LM Studio, but text performance is quite solid. Asked it to create a landing page for a tech event. Video is fast forwarded for the demo.
Watch video
- 12
@DataChaz ·
SOMEONE GOT A 744-BILLION-PARAMETER AI MODEL RUNNING ON A LAPTOP COLIBRI runs GLM-5.2 on a 25GB machine with no GPU by keeping the active parameters in RAM and streaming the rest from disk as needed. Sure, it is not fast, but getting near-perfect responses from a model this large on consumer hardware is an incredible proof of concept. Apache-2.0. 24k stars Free and open source. Repo in 🧵↓
- 13
- 14
@betomoedano ·
Playing around with local AI 🤓 qwen3.6-27b runs smoothly thanks to @lmstudio and MLX. It generates decent results, I'm surprised. Video dropping soon! 🔜
Watch video
- 15
@sharbel ·
Anthropic's API can run $1000+/month at heavy Claude Code usage. Someone built Free Claude Code: A proxy that lets Claude Code talk to free or local model providers instead. It's called free-claude-code. 7,300+ stars on GitHub. Set two environment variables (ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN), point Claude Code at the proxy, done. The CLI and VSCode extension keep working, but behind the scenes you're hitting a different model, not Claude. What it supports: → Routes to NVIDIA NIM's free tier (rate-limited). → OpenRouter, including free model tiers. → DeepSeek direct API. → LM Studio / llama.cpp for fully local inference. → Per-model routing: different provider per Claude model tier. → Thinking-token parsing into Claude thinking blocks. → Heuristic tool-call parser for models without native tool use. → Discord / Telegram bot for remote coding sessions. Real trade-offs to know: - Quality drops. Backing models are not Claude. Tool use is less reliable (that's why the heuristic parser exists). Agentic tasks that Sonnet or Opus nail will fail more often on free tiers. - Free tiers aren't permanent. NIM / OpenRouter / DeepSeek quotas and pricing can change. - Data path changes. Your prompts and code now flow through whichever provider you route to, with their retention policy, not Anthropic's. Worth it for tinkering, offline work, or non-sensitive side projects. Not a drop-in replacement for paid Claude on client code or serious engineering. If your actual goal is "pay less for Claude Code at full quality," the Claude Pro sub ($20/mo) or Max tier ($100-200/mo) includes Claude Code usage directly, no API key needed. MIT licensed. 100% open source.
- 16
@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👇
- 17
- 18
@_vmlops ·
GEMMA 4 12B JUST CHANGED LOCAL AI DEVELOPMENT google dropped an encoder-free multimodal model no separate vision encoder. no audio encoder. just one decoder-only transformer handling everything ▫️ raw pixel patches projected directly to LLM hidden dim ▫️ raw 16kHz audio sliced into 40ms frames, fed straight in ▫️ LoRA/full fine-tuning updates all modalities in a single pass ▫️ runs locally on 16GB VRAM ▫️ OpenAI-compatible local API server via `litert-lm serve` the wild part...? it can process 5 minutes of video at 1FPS with audio, and build a working Gradio app using itself as the backend one model. vision + audio + text. zero encoder overhead. available on Hugging Face, Ollama, LM Studio & vLLM today
Watch video
- 19
- 20
@ManningBooks ·
Working with sensitive data? That doesn't automatically rule out AI — it may just mean running it locally. Local models keep your data on your machine, and for many focused tasks, smaller models can produce highly relevant results. With tools like @Ollama, getting started is easier than you might think. Dr. Errol Brandt, founder of Kiraa AI, explains why local AI is worth considering. Learn more in Build Applications with Local AI Models on a Mac by Keiji Kamigusa (@keiji_dl & @JoshuaKCage1): https://t.co/lboeqrCc5n
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- 21
@AlphaSignalAI ·
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.
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- 22
@lemire ·
AMD is coming up with its small AI PC (AI Halo). It will compete against NVIDIA DGX Spark. Both look a bit like a mac Mini. Just a tiny box. The AI Halo should cost US$4000, so it is accessible to hobbyists and small IT departement. Set it up with LM Studio with its llama.cpp backend and you got yourself a 'ChatGPT-like' experience from your own network. Both the AI Halo and the Spark have 128GB of 'unified' RAM (meaning that the memory is shared between CPU and GPU). It is the same type of memory you get on your macBook (LPDDR5X), so it is not the fancy high-bandwidth memory you get on GPU cards... but it is good enough. So I played with llama.cpp and I was running open source models last year. It is pretty cool getting this silly little C++ program (llama.cpp) that answers you back when you ask questions... right there on your machine. But I think that most people will be somewhat disappointed in practice. It is close to Big AI models, but still obviously inferior to even the cheap commercial offerings. If you have US$4000 to burn, you are better off spending it on tokens from an AI provider. That's short term. Long term... Who knows? Linux started out as a toy. It is now running the Internet. Give it 5, 10, 20 years and maybe these little boxes could be everywhere.
- 23
@SaiyamPathak ·
Yesterday in the video I took - the qwen32B which was a dense 32B and all active parameters for every token whereas for the MLX version it was A3B - active 3B. this morning I ran some tests again: - Qwen3.5 (NVFP4, MLX): 23.11 tok/s decode - Nemotron (GGUF, llama.cpp): 21.70 tok/s decode So not that much of a difference - maybe llamacpp already good for M1 type - I need to test it across all architectures though. Interestingly I tested the LMstudio as well that had the MLX support for quite some time now. results are pretty interesting and I tried kind of similar models although its not apples to apples. LM Studio - MLX - Qwen3.5-35B-A3B-4bit - 33.82 tok/s Ollama - MLX - qwen3.5:35b-a3b-coding-nvfp4 - 23.11 tok/s the 4-bit quantization is different here for both models. Can you share your benchmarks? anything anyone tested.
- 24
- 25
@frog_omo ·
you can run chatgpt on your laptop without internet. no subscription. no API. no data leaving your machine. here's the 15-minute setup: step 1: download an app pick one: → LM Studio (recommended for beginners) → Jan → GPT4All → Ollama + Open WebUI (if you want browser interface) step 2: download a model start with one of these: → Llama 3.2 3B — best all-rounder for laptops → Qwen 2.5 3B — great for writing → Gemma 2 2B — fastest, smallest → Mistral 7B — best quality (needs 16GB RAM) step 3: start chatting that's it. no account. no API key. what "3B" and "7B" mean: 3B = 3 billion parameters = runs on 8GB RAM 7B = 7 billion parameters = needs 16GB RAM smaller = faster but less capable larger = smarter but slower what local models handle: ✓ summarising documents ✓ drafting emails ✓ brainstorming ideas ✓ explaining code ✓ everyday writing what they don't handle: ✗ complex reasoning ✗ current events (no internet) ✗ tasks requiring GPT-4 level thinking why run AI locally: → privacy — nothing uploaded anywhere → offline — works on planes, bad wifi, anywhere → free — no monthly subscription → unlimited — no rate limits my recommendation: download LM Studio → install → search "Llama 3.2 3B" → download → chat fifteen minutes from now you'll have a private AI assistant that works offline. your data never leaves your laptop.
- 26
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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.
Watch video
- 28
@JulianGoldieSEO ·
STOP PAYING FOR HERMES AGENT TOKENS. There are 4 ways to run it for free, but one setup is much easier than the rest. The 4 Free Methods: → Nous Portal regularly rotates in free models like Step 3.7 Flash and Nemotron 3 Ultra → OpenRouter gives you free APIs for models like Hermes 3 405B Instruct, Llama and North Mini Code → Ollama and LM Studio let you run local models with $0 token costs and better privacy → OAuth lets you reuse existing ChatGPT or Grok subscriptions without paying extra API fees The Best Workflow: ✓ Create a separate Hermes profile for each provider or model ✓ Use Blank Slate profiles to remove unnecessary tools and reduce token usage ✓ Use LM Studio’s hardware grading to avoid downloading models that are too large for your machine ✓ Switch models based on the task instead of getting locked into one provider My ranking: 1. OpenRouter 2. Local models 3. Nous Portal 4. OAuth The real advantage is not just free access. It is being able to loop agents all day, protect private data and swap models without rebuilding your setup.
Watch video
- 29
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- 31
@smratitiwa86867 ·
Holy shit...Someone tried replacing Claude with a local LLM… …and waited 13 minutes for THIS: > “I am a large language model, trained by Google.” That’s it. 13 minutes. One useless sentence. Let’s be honest — we’ve ALL had this thought: “Why am I paying for Claude when I can just run open-source models locally for free?” So he tried. Ran Gemma locally. No API costs. Full control. Sounds smart, right? Here’s what actually happened: Before he even asked a real question… The model was already drowning. Tools like Claude Code don’t start from zero. They load 20,000+ tokens of system context: • instructions • memory • tools • workflows Your “free” local model? It’s choking before the race even starts. And then you wait… …and wait… …and wait… …for a generic sentence you could’ve gotten in 0.8 seconds. This is the part no one tells you: Running LLMs locally isn’t just about compute. It’s about: context handling latency orchestration tooling You’re not replacing an API. You’re trying to replace an entire AI infrastructure stack. The API bill hurts. But wasting hours waiting for responses? That’s way more expensive.
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@JulianGoldieSEO ·
STOP PAYING TOKENS TO BUILD WITH CODEX. The trick is so simple most people completely miss it. The Setup: → Codex is just the hands. It writes files, runs commands, checks its own work. → The expensive part isn't Codex. It's the AI model doing the thinking. → Swap the default model provider in one config file and point Codex to a free model instead. The Free Brain: ✓ Use OpenRouter to access multiple free coding models with one API key. ✓ Or run Qwen Coder, DeepSeek Coder, or GLM locally with Ollama or LM Studio. ✓ Nothing is hacked. Codex already supports changing model providers. Why This Suddenly Works: → Older free models constantly failed tool calls. → Newer open models can reliably open files, run commands, and complete multi-step jobs. → Goal Mode can now keep working for hours instead of dying when paid tokens run out. The Bonus Layer: ✓ Agent OS adds memory. ✓ Saves every project. ✓ Auto-approve mode. ✓ One-click model switching. ✓ Goal Mode for long-running builds. Free hands + free brain = one of the cheapest AI coding setups available today.
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- 34
@WellerOlaf ·
Running Qwen 3.5 9B locally on my ThinkPad P14s (Quadro T500) via LM Studio. Pretty impressive what open-source models can do on a laptop now. Speed I’m seeing: ~5 tok/s for simple prompts ~1.4 tok/s for harder reasoning tasks Test prompt: A bat and a ball cost $1.10 together. The bat costs $1 more than the ball. How much does the ball cost? Explain your reasoning.
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