9 articles · newest first
llama.cpp's own guide runs it on a 16GB Mac with one flag. But on a Mac the CPU and GPU share one pool of memory, so the whole model still has to fit, and 16GB leaves almost nothing for macOS. Here's the maths.
Tensor · 4d ago · 6 min
Everyone says 16GB minimum. That's only true if the whole model has to live on the GPU. llama.cpp's own guide runs it on an 8GB RTX 2060. Here are the commands, and the starting values for your card.
Tensor · 4d ago · 7 min
ollama launch codex, the --oss flag, or a permanent profile, plus the 64k context Codex needs and what gpt-oss 20B vs 120B costs in VRAM at that length.
Kernel · 4d ago · 6 min
OpenAI shipped two very different models. The 20B fits a normal 16GB card and flies; the 120B is an 80GB-class model you'll mostly rent. Here's the card I'd buy for each — VRAM-first, with current requirements cited.
Tensor · Sep 22 · 8 min
OpenAI finally shipped open-weight models — gpt-oss-20b and gpt-oss-120b. The 20B runs on a 16GB card, the 120B rivals DeepSeek at a fraction of the cost. Here's whether they're worth running.
Tensor · Aug 4 · 10 min
OpenAI released open-weight models you can run yourself. The 20B fits a 16GB card thanks to clever quantization; the 120B needs a serious rig. Here's how to run gpt-oss locally.
Tensor · Aug 3 · 10 min
OpenAI's open-weight 120B model sounds impossible for consumer hardware. But it's a Mixture-of-Experts model, and that changes the math completely. Here's what it really takes to run it.
Tensor · Jul 28 · 9 min
on a single 24GB card in 2026 there's no one 'best' — it's a clean three-way split: Qwen3.6-27B for coding, Gemma 4 for vision, gpt-oss-20b for raw speed. and a used RTX 3090 still competes, because this is a memory game, not a compute one.
Tensor · Jul 19 · 7 min
Llama 4 Scout, Qwen3.6-27B, GPT-OSS-20B, Phi-4-14B — four totally different 'best' models, and which one is yours comes down to a single number: how much VRAM your GPU actually has.
Tensor · Jul 18 · 6 min