What GPU do I need to run Qwen/Qwen3-VL-235B-A22B-Instruct?
235.7B parameters, published in BF16. View on Hugging Face
Set up Qwen3-VL-235B-A22B-InstructQwen3-VL-235B-A22B-Instruct is published by Qwen on Hugging Face, with 825,459 downloads and 415 likes to date. It's a Qwen3VLMoeForConditionalGeneration 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 439.0 GB | 526.8 GB | A100 | 7 | $8.09/hr |
| FP8 (quantized) | 219.5 GB | 263.4 GB | RTX 4090 | 6 | $2.89/hr |
| INT4 (quantized) | 109.7 GB | 131.7 GB | RTX A5000 | 6 | $1.06/hr |
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 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-235B-A22B-Instruct at its published (BF16) precision: 7× A100, at $1.16/hr per GPU ($8.09/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3-VL-235B-A22B-Instruct: common questions
Can Qwen3-VL-235B-A22B-Instruct run on a single GPU?
No. At BF16 it needs 526.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 80.0 GB A100, and it takes 7 of them.
How many GPUs do I need to run Qwen3-VL-235B-A22B-Instruct?
7 at BF16. It needs 526.8 GB of VRAM and the cheapest capable live offer is a 80.0 GB A100, so 7 of them come to $8.09/hr in total.
Does quantizing Qwen3-VL-235B-A22B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is 7 A100 cards at $8.09/hr. At INT4 (quantized) it drops to 6 RTX A5000 cards at $1.06/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen models
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
- Qwen2.5-7B-Instruct (7.6B, BF16)
- Qwen3-VL-8B-Instruct (8.8B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
