@DivyanshT91162: Local LLMs just hit a whole new level This Hugging Face release is actually insane: "gpt-oss-20b-tq3" An official 20B+ …
Summary
A new 20B+ parameter MoE model from OpenAI, quantized to 3-bit via TurboQuant and optimized with MLX, allows for high-performance local LLM inference on standard 16GB MacBooks.
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@hank_aibtc: Family, local LLMs are incredibly impressive! I stumbled upon this gpt-oss-20b-tq3 on Hugging Face, and it's truly captivating! OpenAI's official open-source 20B+ parameter MoE model, optimized by the community using TurboQuant 3-bit quantization + MLX...
The article highlights the gpt-oss-20b-tq3 model, a quantized version of an OpenAI MoE model that runs efficiently on standard 16GB MacBook Airs using TurboQuant and MLX optimizations.
@tom_doerr: Runs 70B LLMs on single 4GB GPU https://github.com/lyogavin/airllm
AirLLM is an open-source tool that optimizes inference memory usage, enabling 70B LLMs to run on a single 4GB GPU without quantization, and supports 405B models on 8GB VRAM.
@ClementDelangue: Local open-weight AI on a laptop has been improving more than twice as fast as Moore's Law! Between May 2024 and May 20…
Hugging Face CEO Clement Delangue claims local open-weight AI performance on laptops is improving 4.7x faster than Moore's Law, citing progress from Llama 3 70B to DeepSeek V4 Flash on unchanged hardware.
@UnslothAI: GLM-5.2 can now be run locally! The 2-bit model retains ~82% accuracy after we shrunk it from 1.51TB to 238GB (-84% siz…
UnslothAI announces GLM-5.2, Z.ai's strongest open model with 744B parameters, now runnable locally via dynamic GGUF quantization reducing size by ~84% to 239GB while retaining ~82% accuracy. It fits on 256GB Macs and supports long-context, reasoning, and agentic tasks.
I benchmarked 21 local LLMs on a MacBook Air M5 for code quality AND speed
A developer benchmarked 21 local LLMs on MacBook Air M5 using HumanEval+ and found Qwen 3.6 35B-A3B (MoE) leads at 89.6% with 16.9 tok/s, while Qwen 2.5 Coder 7B offers the best RAM-to-performance ratio at 84.2% in 4.5 GB. Notably, Gemma 4 models significantly underperformed expectations (31.1% for 31B), possibly due to Q4_K_M quantization effects.