How much VRAM does Qwen3.6 27B need?

About 19.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 21.0 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Qwen3.6 27B run on a 24GB GPU?

Yes — at Q4_K_M and 4K context it needs about 19.1 GB, which leaves 4.9 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.

Which quantization of Qwen3.6 27B should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 3 levels in the index are Q4_K_M, Q5_K_M, Q8_0.

Qwen3.6 27B

27B

Alibaba Qwen3.6

The dense model of the Qwen3.6 generation and a popular local coding model. It has the same layout as Qwen3.8 27B, the newer release: only 16 of its 64 layers keep a growing KV cache. At Q4_K_M it needs about 19.1 GB at 4K context and 21.0 GB at 32K, comfortable on a 24 GB card either way. Q5_K_M is tight on 24 GB, and a 16 GB card cannot hold it at any level listed here. Sizes come from bartowski's published GGUF files; Unsloth's builds of the same levels are about 1 GB smaller. No per-level quality loss has been published.

⬇ 52.1K HF downloads♥ 89 likesbartowski/Qwen_Qwen3.6-27B-GGUF· stats from 10/11/2026

Consumer GPUPro GPUMac / Apple Silicon

262K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	5.25	19.1 GB	—	—	Estimated	Calc HF
GGUF	Q5_K_M	6.12	22.2 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	30.7 GB	—	—	Estimated	Calc HF

Similar models

Compare with Qwen3.6 35B-A3B

35B-A3B

Qwen3.6 35B-A3B

Alibaba Qwen3.6

Consumer GPUPro GPU

23.4 GBmin VRAM—accuracy

One of the most recommended models for a 24 GB card this year: 35.6B parameters, about 3B active per token, so it reads per token about as much as a 3B model. Only 10 of its 40 layers keep a growing KV cache, so context is cheap: at Q4_K_M it needs about 23.4 GB at 4K and 24.0 GB at 32K. On this site's rule that is tight on a 24 GB card, not comfortable, because it counts a working buffer most guides leave out. A 32 GB card, or a 48 GB Mac through the GPU's share of unified memory, has real room. It reads images through a separate vision encoder. Sizes come from the published GGUF file sizes, and no per-level quality loss has been published.

26B-A4B

Gemma 4 26B-A4B IT

Google Gemma 4

Consumer GPUPro GPU

15.3 GBmin VRAM—accuracy

Google's Gemma 4 mixture-of-experts model: about 25B parameters in the text model, roughly 4B active per token, a 256K window and image input. Only 5 of its 30 layers keep a cache that grows with context, and those use fewer, wider heads, so the cache stays small — about 0.9 GB at 32K and 5.3 GB at the full 256K. At Q4 that is about 17 GB at 32K: comfortable on a 24 GB card, which can still load the whole window (tight). A 16 GB card is just too small. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.

31B

Gemma 4 31B IT

Google Gemma 4

Consumer GPUPro GPU

18.6 GBmin VRAM—accuracy

Google's dense Gemma 4 flagship: about 31B parameters, a 256K window and image input. Only 10 of its 60 layers cache the full context, and those use four wide KV heads, so 128K of context adds about 11 GB of cache where a conventional 60-layer design would need about 120 GB. At Q4 it is about 21 GB at 4K — on a 24 GB card that is right at the edge of comfortable, and tight from 32K. A 32 GB card holds 32K comfortably. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.

30B

Muse Glimmer 30B

Meta Muse

Consumer GPUMac / Apple Silicon

17.7 GBmin VRAM—accuracy

Meta's first open-weight model since Llama 4, released in August under Apache 2.0 and built to run on one card. It is a dense model: a 28B text decoder that reads images through a separate vision encoder and answers in text. Three of every four layers use a 2K sliding window and the rest share just 2 KV heads, so long context is unusually cheap. At Q4_K_M it needs about 17.7 GB at 4K context and only 19.4 GB at its full 128K window, comfortable on a 24 GB card either way. Q5_K_M is tight on 24 GB, and Q8_0 wants a 32 GB card. The vision encoder (about 1.4 GB as a GGUF mmproj file) and Meta's optional speculative-decoding drafter load on top and are not included. Sizes come from the published GGUF file sizes; the Q4_K_M row is Meta's own official build. Meta's benchmark claims are its own, and no per-level quality loss has been published.

Running Qwen3.6 27B locally

At Q4_K_M and 4K of context, Qwen3.6 27B needs about 19.1 GB — 17.1 GB of weights, 0.25 GB of KV cache and a 1.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 31 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.75 GB, taking the total to 21.0 GB. That is gentler than the parameter count suggests: only 16 of 64 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 256K; holding all of it at Q4_K_M would need about 36 GB.

Which build to download

This index only tracks Qwen3.6 27B in GGUF, across 3 levels (Q4_K_M, Q5_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Qwen3.6 27B need?: About 19.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 21.0 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Qwen3.6 27B run on a 24GB GPU?: Yes — at Q4_K_M and 4K context it needs about 19.1 GB, which leaves 4.9 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.
Which quantization of Qwen3.6 27B should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 3 levels in the index are Q4_K_M, Q5_K_M, Q8_0.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 309024GB · +4.9 Radeon RX 7900 XTX24GB · +4.9 RTX 409024GB · +4.9 Tesla P40 24G24GB · +4.9 Mac M5 32G32GB · +4.9
Tight but possible: Radeon RX 7900 XT20GB
Just misses: Mac M4 24G+1.1GB over Mac M4 Pro 24G+1.1GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-11 RSS → /feed.xml