What GPU do I need to run lmstudio-community/Qwen3-VL-4B-Instruct-MLX-4bit?
1.0B parameters, published in BF16. View on Hugging Face
Qwen3-VL-4B-Instruct-MLX-4bit is published by lmstudio-community on Hugging Face, with 106,868 downloads and 10 likes to date. It's a Qwen3VLForConditionalGeneration model built for image-text-to-text, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Qwen3-VL-4B-Instruct-MLX-4bit at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More lmstudio-community models
- gemma-3n-E4B-it-MLX-bf16 (7.8B, BF16)
- Qwen3-1.7B-MLX-bf16 (1.7B, BF16)
- LFM2-1.2B-MLX-bf16 (1.2B, BF16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
