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.

⬇ 4.8M HF downloads♥ 1044 likesQwen/Qwen3-VL-8B-Instruct· stats from 8/14/2026

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 = site benchmarks · Estimated = formula · 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 VRAM·99.7%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.

12B