qwen

8 articles · newest first

Editorial infographic of three model badges converging on a single 24GB graphics card with a fork in the path, near-black background, indigo-violet with a coral accent

Which 24GB-class model should you run? Mistral Small 24B vs Qwen3.6 27B vs Gemma 4 31B

You've got a 24GB card and three excellent dense models that fit it. Here's how Mistral Small 24B, Qwen3.6 27B and Gemma 4 31B differ on VRAM, context and modality — and a clear pick for each kind of user.

Tensor · 6d ago · 7 min

Isometric diorama of a code editor window and a graphics card together on a desk with a small padlock, near-black background, indigo-violet with a coral accent

Qwen2.5-Coder-32B locally: a private Copilot on a 24GB card

Qwen2.5-Coder-32B still runs a serious private coding assistant on a single 24GB card, with a 32K native context (128K with YaRN). The VRAM math, the tight-but-real fit, and how it stands against the newer Qwen3-Coder.

Tensor · 6d ago · 7 min

Editorial infographic of a solid dense 27B model block fitting neatly inside a 24GB graphics card, near-black background, indigo-violet with a coral accent

Qwen3.6 27B locally: the all-dense 24GB Qwen

Qwen3.6's dense 27B is the no-fuss 24GB-card option — every parameter fires, nothing to offload, a real 256K context. The VRAM math, the GPU, and how it differs from the 35B-A3B MoE.

Tensor · 6d ago · 7 min

Isometric diorama of a huge grid of expert blocks overflowing off a single graphics card into several system-RAM sticks, near-black background, indigo-violet with a coral accent

Qwen3-Next 80B-A3B locally: an 80B at home, if you can hold it

Qwen3-Next 80B-A3B activates only 3B parameters per token — so it's fast. But 80B of weights still have to live somewhere. The honest VRAM math, the offload trick, and what it really takes.

Tensor · 6d ago · 7 min

Isometric diorama of a grid of many small expert blocks with only a few lit up feeding a token, near-black background, indigo-violet with a coral accent

Qwen3.6 35B-A3B locally: a 35B that thinks like a 3B

Qwen3.6's default is a mixture-of-experts model: 35B on disk, 3B active per token. That makes it fast — but the VRAM math has a twist worth understanding before you buy a card.

Tensor · 6d ago · 7 min

Editorial infographic of a compact AI model badge flowing into a mid-range GPU that comfortably fits it, near-black background, indigo-violet with a coral accent

Qwen3.5 9B locally: the model 'ollama run qwen3.5' actually gives you

Type ollama run qwen3.5 and this is the model you get — the 9B. What it is, how little VRAM it needs, and the cheapest card that runs it well.

Tensor · 6d ago · 7 min

rows of server infrastructure representing large-scale cloud computing

Hardware

Alibaba just open-sourced a 2.4 trillion parameter AI model — good luck ever running it yourself

it beats Claude on some benchmarks and rivals GPT on others, and it's free to download next week. here's the hardware math that ruins the fun.

Voltage · Aug 3 · 7 min

Two graphics cards side by side, illustrating GPU VRAM tier comparison

everyone asks which local AI model is 'best.' wrong question — your VRAM already picked it

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