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THIS AMERICAN DEVELOPER SPENT WEEKS DEBUGGING TIMEOUT ERRORS IN OLLAMA. THEN HE LOOKED UNDER THE HOOD LM Studio is just llama.cpp Ollama is just llama.cpp so he cloned llama.cpp from source, pulled Qwen 3.6 35B off Hugging Face, set up asymmetric KV quantization and got a local server running...

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leopardracer

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238,296 görüntüleme • 21 gün önce •via X (Twitter)

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THIS DEVELOPER HASN’T PAID AN API BILL IN 3 MONTHS. HIS AGENTS RAN 10,000 TIMES FOR FREE he built a local AI lab under his desk two GPUs 32GB VRAM zero rate limits his agents loop 400 times if they want to his coworkers are still watching the usage dashboard the only thing separating them: llama.cpp + llama-swap every prompt stays on his machine every experiment costs $0 zero invoices bookmark & like this before your next API bill hits
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THIS DEVELOPER HASN’T PAID AN API BILL IN 3 MONTHS. HIS AGENTS RAN 10,000 TIMES FOR FREE he built a local AI lab under his desk two GPUs 32GB VRAM zero rate limits his agents loop 400 times if they want to his coworkers are still watching the usage dashboard the only thing separating them: llama.cpp + llama-swap every prompt stays on his machine every experiment costs $0 zero invoices bookmark & like this before your next API bill hits

leopardracer

538,234 görüntüleme • 21 gün önce

Real-time webcam demo with Hugging Face SmolVLM and ggml llama.cpp server. All running locally on a Macbook M3
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Real-time webcam demo with Hugging Face SmolVLM and ggml llama.cpp server. All running locally on a Macbook M3

Xuan-Son Nguyen

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testing Qwen3.5-35B-A3B latest optimized version by UnslothAI on a single RTX 3090. one detailed prompt. zero handholding. watch a 3B model scaffold an entire multifile game project autonomously. the setup: > model: Qwen3.5-35B-A3B (80B total, only 3B active per token) > quant: UD-Q4_K_XL by Unsloth (MXFP4 layers removed in latest update) > speed: 112 tok/s generation, ~130 tok/s prefill > context: 262K tokens > flags: -ngl 99 -c 262144 -np 1 --cache-type-k q8_0 --cache-type-v q8_0 > engine: llama.cpp > agent: Claude Code talk to localhost:8080 (llama.cpp now has native Anthropic API endpoint. no LiteLLM needed) q8_0 KV cache cuts VRAM usage in half vs f16 at 262K. -np 1 is default but worth noting. parallel slots multiply KV cache and at 262K that's an instant OOM. the prompt was more detailed than this but you get the idea: build a space shooter with parallax backgrounds, particle systems, procedural audio, 4 enemy types, boss fights, power-up system, and ship upgrades. 8 JavaScript modules. no libraries. game's called Octopus Invaders. gameplay footage dropping next.
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testing Qwen3.5-35B-A3B latest optimized version by UnslothAI on a single RTX 3090. one detailed prompt. zero handholding. watch a 3B model scaffold an entire multifile game project autonomously. the setup: > model: Qwen3.5-35B-A3B (80B total, only 3B active per token) > quant: UD-Q4_K_XL by Unsloth (MXFP4 layers removed in latest update) > speed: 112 tok/s generation, ~130 tok/s prefill > context: 262K tokens > flags: -ngl 99 -c 262144 -np 1 --cache-type-k q8_0 --cache-type-v q8_0 > engine: llama.cpp > agent: Claude Code talk to localhost:8080 (llama.cpp now has native Anthropic API endpoint. no LiteLLM needed) q8_0 KV cache cuts VRAM usage in half vs f16 at 262K. -np 1 is default but worth noting. parallel slots multiply KV cache and at 262K that's an instant OOM. the prompt was more detailed than this but you get the idea: build a space shooter with parallax backgrounds, particle systems, procedural audio, 4 enemy types, boss fights, power-up system, and ship upgrades. 8 JavaScript modules. no libraries. game's called Octopus Invaders. gameplay footage dropping next.

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You can now have an AI researcher running on your laptop 24/7 for free! Running Qwen3-35B-A3B with llama.cpp and a 4-bit quant from Unsloth
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You can now have an AI researcher running on your laptop 24/7 for free! Running Qwen3-35B-A3B with llama.cpp and a 4-bit quant from Unsloth

Lewis Tunstall

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Running Phi 4 Mini Instruct at 75 tok/sec locally on a Mac - powered by llama.cpp! 🔥 All you need is: brew install llama.cpp Followed by: llama-cli -hf bartowski/microsoft_Phi-4-mini-instruct-GGUF:Q8_0 That's it! 🤗
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Running Phi 4 Mini Instruct at 75 tok/sec locally on a Mac - powered by llama.cpp! 🔥 All you need is: brew install llama.cpp Followed by: llama-cli -hf bartowski/microsoft_Phi-4-mini-instruct-GGUF:Q8_0 That's it! 🤗

Vaibhav (VB) Srivastav

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Stop paying for Claude Code! Use it for FREE with ollama I just set up Claude Code running locally on my computer. Zero API costs, complete privacy, and it actually works for real coding tasks. Full step-by-step guide in my latest video:
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Stop paying for Claude Code! Use it for FREE with ollama I just set up Claude Code running locally on my computer. Zero API costs, complete privacy, and it actually works for real coding tasks. Full step-by-step guide in my latest video:

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There's a free 120 billion parameter AI you can run on your own computer. Zero internet. Zero API costs. Zero monthly fees. It's called Nvidia Nemotron 3 Super. And it just changed what solopreneurs can do with AI agents. Here's the setup: → Download Ollama. Copy one command into your terminal. → Connect it to OpenClaw in under 10 minutes. → Your AI agent now runs 24/7 on WhatsApp, Discord, or Telegram. → It answers customers, runs tasks, and manages workflows while you sleep. → 256,000 token context window. It reads entire documents without forgetting. No subscriptions. No cloud. No one else touching your data. A full AI employee stack for $0. Save this. Set it up this weekend.
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There's a free 120 billion parameter AI you can run on your own computer. Zero internet. Zero API costs. Zero monthly fees. It's called Nvidia Nemotron 3 Super. And it just changed what solopreneurs can do with AI agents. Here's the setup: → Download Ollama. Copy one command into your terminal. → Connect it to OpenClaw in under 10 minutes. → Your AI agent now runs 24/7 on WhatsApp, Discord, or Telegram. → It answers customers, runs tasks, and manages workflows while you sleep. → 256,000 token context window. It reads entire documents without forgetting. No subscriptions. No cloud. No one else touching your data. A full AI employee stack for $0. Save this. Set it up this weekend.

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llama.cpp with MTP support makes local models fast enough to use as daily drivers 🚀 Qwen3.6-27B dense generation (on A10G): From 25 tok/s → 45 tok/s (+78%). Two flags on llama-server: --spec-type draft-mtp --spec-draft-n-max 2
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llama.cpp with MTP support makes local models fast enough to use as daily drivers 🚀 Qwen3.6-27B dense generation (on A10G): From 25 tok/s → 45 tok/s (+78%). Two flags on llama-server: --spec-type draft-mtp --spec-draft-n-max 2

Victor M

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Let me demonstrate the true power of llama.cpp: - Running on Mac Studio M2 Ultra (3 years old) - Gemma 4 26B A4B Q8_0 (full quality) - Built-in WebUI (ships with llama.cpp) - MCP support out of the box (web-search, HF, github, etc.) - Prompt speculative decoding The result: 300t/s (realtime video)
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Let me demonstrate the true power of llama.cpp: - Running on Mac Studio M2 Ultra (3 years old) - Gemma 4 26B A4B Q8_0 (full quality) - Built-in WebUI (ships with llama.cpp) - MCP support out of the box (web-search, HF, github, etc.) - Prompt speculative decoding The result: 300t/s (realtime video)

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You can now run OpenClaw completely free using Qwen 3.6 Plus 🤯 Just plug Alibaba’s free API into OpenClaw, Claude Code, Hermes or Kilo Code… and suddenly you’ve got a powerful AI agent with zero cost. This might be the best free AI setup right now. 🚀
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You can now run OpenClaw completely free using Qwen 3.6 Plus 🤯 Just plug Alibaba’s free API into OpenClaw, Claude Code, Hermes or Kilo Code… and suddenly you’ve got a powerful AI agent with zero cost. This might be the best free AI setup right now. 🚀

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Advice for AI engineers 💡 A local AI assistant is just a while loop, an LLM and a set of tools. Here's a step-by-step lesson on how to build one from scratch with Liquid AI LFM2-24B-A2B running on llama.cpp. No cloud APIs. Enjoy ↓
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Advice for AI engineers 💡 A local AI assistant is just a while loop, an LLM and a set of tools. Here's a step-by-step lesson on how to build one from scratch with Liquid AI LFM2-24B-A2B running on llama.cpp. No cloud APIs. Enjoy ↓

Pau Labarta Bajo

15,463 görüntüleme • 1 ay önce

i pointed hermes agent at nvidia's nemotron cascade 2 30B-A3B on a single RTX 3090 24GB. IQ4_XS quant by bartowski, 187 tok/s, 625K context. had it discover its own hardware, create an identity file, then build a full GPU marketplace UI from a single prompt. it one shotted it. first attempt no iteration. qwen 3.5 35B-A3B on the same hardware same 3090 24GB took an iteration to recover from a blank screen on the same type of build. 24 days between these two models releasing. same active parameters, completely different architectures and cascade 2 through hermes agent just keeps going. this model goes on and on. feast your eyes. more iterations and tests dropping soon. nvidia really cooked. no special flags needed. nvidia optimized this mamba MoE so well it just runs. flash attention auto enabled, context auto allocated. the model does the work not the config. but i compiled llama.cpp from source and i'm not sure how it performs on other engines. if you ran nemotron on any hardware drop your numbers below. RTX, AMD, Mac, whatever. model, quant, tok/s, engine. i want to see if it holds everywhere or just on llama.cpp.
1:31

i pointed hermes agent at nvidia's nemotron cascade 2 30B-A3B on a single RTX 3090 24GB. IQ4_XS quant by bartowski, 187 tok/s, 625K context. had it discover its own hardware, create an identity file, then build a full GPU marketplace UI from a single prompt. it one shotted it. first attempt no iteration. qwen 3.5 35B-A3B on the same hardware same 3090 24GB took an iteration to recover from a blank screen on the same type of build. 24 days between these two models releasing. same active parameters, completely different architectures and cascade 2 through hermes agent just keeps going. this model goes on and on. feast your eyes. more iterations and tests dropping soon. nvidia really cooked. no special flags needed. nvidia optimized this mamba MoE so well it just runs. flash attention auto enabled, context auto allocated. the model does the work not the config. but i compiled llama.cpp from source and i'm not sure how it performs on other engines. if you ran nemotron on any hardware drop your numbers below. RTX, AMD, Mac, whatever. model, quant, tok/s, engine. i want to see if it holds everywhere or just on llama.cpp.

Sudo su

70,583 görüntüleme • 2 ay önce

Claude Code is now completely free forever 🤯 Google just dropped Gemma 4 on Ollama, which means you can run Claude Code locally with zero API bills and zero subscriptions. Just install Gemma 4, connect it to Claude Code, and suddenly you have free AI agents running directly on your computer. Google may have just killed AI subscription costs. 🚀
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Claude Code is now completely free forever 🤯 Google just dropped Gemma 4 on Ollama, which means you can run Claude Code locally with zero API bills and zero subscriptions. Just install Gemma 4, connect it to Claude Code, and suddenly you have free AI agents running directly on your computer. Google may have just killed AI subscription costs. 🚀

Julian Goldie SEO

605,344 görüntüleme • 2 ay önce

local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.
1:39

local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.

sabesh 📟

27,416 görüntüleme • 13 gün önce

I know some bros want to see what happens when we push the value even further, so here we go: ⚪️ -ncmoe 25 → 41.8 tok/s RAM: 12GB & VRAM: 6.9GB 🔴 -ncmoe 23 → 43.8 tok/s RAM: 12.2GB & VRAM: 7.4GB 🟢 -ncmoe 21 → 38.6 tok/s RAM: 12.4GB & VRAM: 7.8GB 🔵 -ncmoe 19 → 19.8 tok/s RAM: 13.8GB & VRAM: 7.8GB As you can see, there's a sweet spot with the VRAM usage. Play around and monitor to find the right value for your setup, you can use llama.cpp web ui to monitor speeds easily Sweet spot seems to be 25-23 for 8GB VRAM ✅ +40tok/s for Qwen3.6 35B with 64k q8_0 context on a 8GB card is very impressive just by using base llama.cpp, and we didn't even try Turboquant, MTP or Dflash yet! I'll focus on these next 👀 (Server flags and setup in the quoted tweet)
0:29

I know some bros want to see what happens when we push the value even further, so here we go: ⚪️ -ncmoe 25 → 41.8 tok/s RAM: 12GB & VRAM: 6.9GB 🔴 -ncmoe 23 → 43.8 tok/s RAM: 12.2GB & VRAM: 7.4GB 🟢 -ncmoe 21 → 38.6 tok/s RAM: 12.4GB & VRAM: 7.8GB 🔵 -ncmoe 19 → 19.8 tok/s RAM: 13.8GB & VRAM: 7.8GB As you can see, there's a sweet spot with the VRAM usage. Play around and monitor to find the right value for your setup, you can use llama.cpp web ui to monitor speeds easily Sweet spot seems to be 25-23 for 8GB VRAM ✅ +40tok/s for Qwen3.6 35B with 64k q8_0 context on a 8GB card is very impressive just by using base llama.cpp, and we didn't even try Turboquant, MTP or Dflash yet! I'll focus on these next 👀 (Server flags and setup in the quoted tweet)

left curve dev

18,049 görüntüleme • 28 gün önce

Introducing Ollama fully Integrated into VSCode with CodeGPT Now you can download open-source models from ollama with a single click and install them as code assistants in VSCode. In this case, I download the qwen2:0.5b (Qwen) model in under a minute, and it becomes fully functional with just one click. The era of locally running code assistants has begun! 🚀
1:14

Introducing Ollama fully Integrated into VSCode with CodeGPT Now you can download open-source models from ollama with a single click and install them as code assistants in VSCode. In this case, I download the qwen2:0.5b (Qwen) model in under a minute, and it becomes fully functional with just one click. The era of locally running code assistants has begun! 🚀

Daniel San

101,533 görüntüleme • 2 yıl önce

🚨Stop gambling on the wrong local LLM. Most people download a model, watch it crawl, then start over. LLM Hardware Scanner is a CLI tool that scans your actual specs and ranks hundreds of LLMs before you waste the bandwidth: ⤷ Reads your RAM, CPU, and GPU to score real compatibility ⤷ Accounts for MoE activation not just bloated total parameter counts ⤷ Auto-selects the optimal quantization for your exact setup ⤷ Ranks models across quality, speed, context length, and true memory fit ⤷ Works natively with Ollama, llama.cpp, and MLX ⤷ Shows a composite score plus a speed estimate before you download a single byte Not a benchmark site. Not a spec sheet. A hardware-aware ranker that tells you the truth about what will actually run on your machine. One scan. Instant ranking. Zero wasted downloads.
0:21

🚨Stop gambling on the wrong local LLM. Most people download a model, watch it crawl, then start over. LLM Hardware Scanner is a CLI tool that scans your actual specs and ranks hundreds of LLMs before you waste the bandwidth: ⤷ Reads your RAM, CPU, and GPU to score real compatibility ⤷ Accounts for MoE activation not just bloated total parameter counts ⤷ Auto-selects the optimal quantization for your exact setup ⤷ Ranks models across quality, speed, context length, and true memory fit ⤷ Works natively with Ollama, llama.cpp, and MLX ⤷ Shows a composite score plus a speed estimate before you download a single byte Not a benchmark site. Not a spec sheet. A hardware-aware ranker that tells you the truth about what will actually run on your machine. One scan. Instant ranking. Zero wasted downloads.

Charlie Hills

37,499 görüntüleme • 3 ay önce

Kimmy K2.5 just launched a local cloud model. And it runs Open Claw for FREE forever. No API costs. No token limits. No subscriptions. Here's the setup 👇 → Download Ollama at → Install Kimmy K2.5 cloud model. → Launch Open Claw with one command. → Runs locally but uses cloud processing. → Zero laptop slowdown. → Works on older machines perfectly. I just set it up in under 2 minutes. Zero configuration. Zero technical skills. Want the commands? DM me.
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Kimmy K2.5 just launched a local cloud model. And it runs Open Claw for FREE forever. No API costs. No token limits. No subscriptions. Here's the setup 👇 → Download Ollama at → Install Kimmy K2.5 cloud model. → Launch Open Claw with one command. → Runs locally but uses cloud processing. → Zero laptop slowdown. → Works on older machines perfectly. I just set it up in under 2 minutes. Zero configuration. Zero technical skills. Want the commands? DM me.

Julian Goldie SEO

239,692 görüntüleme • 4 ay önce