qwen

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Hand-drawn illustration of a small bonsai tree in a cream pot with coral pruning shears, in front of the faint violet outline of a giant tree on a near-black background

Bonsai 2 27B: How PrismML Shrinks a 27B Model to 5.9 GB

A 27B model that fits in about 6 GB sounds like a trick. It is ternary weights, a shared scale and a rotation. Here is how it works, what PrismML's own tests show, and where the loss hides.

Aliteq Team · Oct 10 · 8 min

Macro photo of rows of identical blank black memory chips on a circuit board, their pins lit in indigo-violet against a near-black background

Qwen3.8 27B Hardware Requirements: VRAM by Quant, and Why Context Costs So Little

A 24 GB card runs Qwen3.8 27B at Q4 or Q5. Q8 needs 32 GB. The surprise is the context cache: 128K tokens costs about 8 GB, a quarter of what an older 32B model charges.

Aliteq Team · Oct 7 · 9 min

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.

Aliteq Team · Sep 26 · 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.

Aliteq Team · Sep 26 · 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.

Aliteq Team · Sep 26 · 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.

Aliteq Team · Sep 26 · 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.

Aliteq Team · Sep 26 · 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.

Aliteq Team · Sep 26 · 7 min

rows of server infrastructure representing large-scale cloud computing

Hardware

Qwen3.8-Max open weights (2.4T-A95B): what it takes to run, and the 27B you can actually run

Alibaba put its Max-class model on Hugging Face as Qwen3.8-2.4T-A95B. The download is 2.5 TB in FP8 and the smallest community quant is still 397 GB. Here is the file-size math, the license, and the sibling that fits a 24 GB card.

Aliteq Team · Aug 3 · 8 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.

Aliteq Team · Jul 18 · 6 min