Qwen3-VL 8B Instruct
8BAlibaba 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.
41K
Max Context
4
Quant Variants
GGUF Q8_0
Best Quality
99.7%
Accuracy Retained
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
Similar models
Compare with Qwen3-VL 30B-A3BQwen3-VL 30B-A3B Instruct
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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 74 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 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; 74 of 76 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 · +1.8RTX 4060 Ti 8G8GB · +1.8Radeon RX 9060 XT 8G8GB · +1.8RTX 5060 Ti 8G8GB · +1.8RTX 50608GB · +1.8
- Tight but possible
- Mac M1 8G8GBMac M3 8G8GB
- Compared here
- GGUF vs AWQ
- A top pick for
- Best local LLM for 8GB
- Try it yourself
- Size it in the calculatorGenerate 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