How much VRAM does Qwen3-VL 8B Instruct need?

About 6.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.5 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 8B Instruct run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 6.2 GB, which leaves 1.8 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.

Which quantization of Qwen3-VL 8B 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, Q5_K_M, Q8_0, AWQ INT4.

Qwen3-VL 8B Instruct

Alibaba Qwen3-VL

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.

⬇ 5.8M HF downloads♥ 1214 likesQwen/Qwen3-VL-8B-Instruct· stats from 10/20/2026

Consumer GPUMac / Apple SiliconCPU / VPS

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	5.9 GB	3.0%	140 tok/s	Community	Calc HF
GGUF	Q5_K_M	5.68	6.9 GB	1.5%	126 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	10.0 GB	0.3%	108 tok/s	Estimated	Calc HF
AWQ	INT4	4	5.3 GB	4.2%	165 tok/s	Estimated	Calc HF

Similar models

Compare with Qwen3-VL 30B-A3B

30B-A3B

Qwen3-VL 30B-A3B Instruct

Alibaba Qwen3-VL

Consumer GPUPro GPU

15.2 GBmin VRAM99.7%accuracy

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.

Qwen3.5 9B

Alibaba Qwen3.5

Consumer GPUMac / Apple Silicon

6.6 GBmin VRAM—accuracy

The newest small Qwen and a common pick for 8 to 16 GB cards. Only 8 of its 32 layers keep a growing KV cache, so it can use a long context on modest hardware. At Q4_K_M it needs about 6.6 GB at 4K and 7.5 GB at 32K, comfortable on an 8 GB card. Its full 256K window needs about 15.2 GB. Q8_0 needs about 10.4 GB, comfortable from 12 GB up. Reasoning is off by default on the small Qwen3.5 sizes, and 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.

12B

Gemma 3 12B IT

Google Gemma 3

Consumer GPUMac / Apple Silicon

8.0 GBmin VRAM98.7%accuracy

Mid-size Gemma 3 with vision. Fits 16GB at Q4; excellent multilingual chat.

12B

Gemma 4 12B IT

Google Gemma 4

Consumer GPUMac / Apple Silicon

7.1 GBmin VRAM—accuracy

Google's mid-sized Gemma 4, released in June: a dense 12B with a 256K window. It takes text, images and audio without separate encoders — the raw inputs go straight into the one decoder. Only 8 of its 48 layers cache the full context, each with a single wide KV head, so the cache stays small: about 1 GB at 32K and 4.5 GB at the full 256K. At Q4_K_M (unsloth's 7.1 GB file) that is about 8.5 GB at 32K and 10.2 GB at 128K, both comfortable on a 12 GB card; a 16 GB card holds the whole window comfortably, and an 8 GB card is tight even at 4K. bartowski's Q4_K_M is a larger file (7.7 GB), so add about half a gigabyte if you use it. Sizes come from the published GGUF files; the image and audio embedders ship as a separate mmproj file, and no per-level quality loss has been published.

Running Qwen3-VL 8B Instruct locally

At Q4_K_M and 4K of context, Qwen3-VL 8B Instruct needs about 6.2 GB — 5.1 GB of weights, 0.56 GB of KV cache and a 0.6 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 76 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 3.94 GB, taking the total to 10.5 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 12 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 165 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 8B Instruct need?: About 6.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.5 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 8B Instruct run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 6.2 GB, which leaves 1.8 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.
Which quantization of Qwen3-VL 8B 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, Q5_K_M, Q8_0, AWQ INT4.

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 40608GB · +1.8 RTX 4060 Ti 8G8GB · +1.8 Radeon RX 9060 XT 8G8GB · +1.8 RTX 5060 Ti 8G8GB · +1.8 RTX 50608GB · +1.8
Tight but possible: Mac M1 8G8GB Mac M3 8G8GB
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 2026-10-20 RSS → /feed.xml