How much VRAM does Qwen3-VL 30B-A3B Instruct need?

About 19.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 22.6 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-VL 30B-A3B Instruct run on a 24GB GPU?

Yes — at Q4_K_M and 4K context it needs about 19.7 GB, which leaves 4.3 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; 29 of 76 do.

Which quantization of Qwen3-VL 30B-A3B Instruct should I use?

Q8_0 has the lowest published quality loss (0.3%), and Q4_K_M is the level most people run. All 4 levels in the index are Q4_K_M, Q3_K_M, Q8_0, AWQ INT4. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

Qwen3-VL 30B-A3B Instruct

30B-A3B

Alibaba Qwen3-VL

Multimodal MoE with only ~3B active parameters, so it stays responsive on Apple unified memory and survives CPU offload far better than a dense 30B. Q4 ~19GB — a 24GB card holds it outright.

⬇ 416.1K HF downloads♥ 610 likesQwen/Qwen3-VL-30B-A3B-Instruct· stats from 10/6/2026

Consumer GPUPro GPUMac / Apple Silicon

41K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

99.7%

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	4.85	19.0 GB	2.6%	95 tok/s	Community	Calc HF
GGUF	Q3_K_M	3.87	15.2 GB	5.4%	104 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	32.6 GB	0.3%	—	Estimated	Calc HF
AWQ	INT4	4	17.2 GB	3.7%	112 tok/s	Estimated	Calc HF

Similar models

Compare with Qwen3-VL 8B

Qwen3-VL 8B Instruct

Alibaba Qwen3-VL

Consumer GPUMac / Apple Silicon

5.3 GBmin VRAM99.7%accuracy

Current-generation vision-language model that still fits a single 8–12GB card at Q4 (~5.9GB). The realistic multimodal option for people without a 24GB GPU — note the vision encoder adds VRAM the KV-cache math below does not model.

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.

27B

Gemma 3 27B IT

Google Gemma 3

Consumer GPUPro GPU

13.0 GBmin VRAM97.2%accuracy

Gemma 3 large instruct with long context and multimodal support. Q4 ~16GB — dual-GPU or 24GB card with short ctx.

Running Qwen3-VL 30B-A3B Instruct locally

At Q4_K_M and 4K of context, Qwen3-VL 30B-A3B Instruct needs about 19.7 GB — 17.6 GB of weights, 0.38 GB of KV cache and a 1.8 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 29 of the 76 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 2.62 GB, taking the total to 22.6 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 40K; holding all of it at Q4_K_M would need about 23 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 4 levels. Q8_0 carries the lowest published perplexity loss at 0.3%. The fastest level listed is AWQ INT4 at about 112 tok/s on an RTX 4090, batch 1 — an estimate, not a run 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-VL 30B-A3B Instruct need?: About 19.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 22.6 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-VL 30B-A3B Instruct run on a 24GB GPU?: Yes — at Q4_K_M and 4K context it needs about 19.7 GB, which leaves 4.3 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; 29 of 76 do.
Which quantization of Qwen3-VL 30B-A3B Instruct should I use?: Q8_0 has the lowest published quality loss (0.3%), and Q4_K_M is the level most people run. All 4 levels in the index are Q4_K_M, Q3_K_M, Q8_0, AWQ INT4. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

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 · +4.3 Radeon RX 7900 XTX24GB · +4.3 RTX 409024GB · +4.3 Tesla P40 24G24GB · +4.3 Mac M5 32G32GB · +4.3
Tight but possible: Radeon RX 7900 XT20GB
Just misses: Mac M4 24G+1.7GB over Mac M4 Pro 24G+1.7GB over
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
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 2026-10-06 RSS → /feed.xml