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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. 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 | AMD MI300X | 3 | $7.89/hr |
| FP8 (quantized) | 219.5 GB | 263.4 GB | RTX PRO 6000 | 3 | $3.96/hr |
| INT4 (quantized) | 109.7 GB | 131.7 GB | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | 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: 3× AMD MI300X, at $2.63/hr per GPU ($7.89/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: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 192,512 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.73 GB |
| 32K | 5.9 GB |
| 128K | 23.5 GB |
| 256K (model maximum) | 47.0 GB |
Computed from the layer, head and window counts in the model's own configuration (Qwen/Qwen3-VL-235B-A22B-Instruct), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
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 192.0 GB AMD MI300X, and it takes 3 of them.
How many GPUs do I need to run Qwen3-VL-235B-A22B-Instruct?
3 at BF16. It needs 526.8 GB of VRAM and the cheapest capable live offer is a 192.0 GB AMD MI300X, so 3 of them come to $7.89/hr in total.
Does quantizing Qwen3-VL-235B-A22B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is 3 AMD MI300X cards at $7.89/hr. At INT4 (quantized) it drops to one AMD MI300X at $2.63/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen3 models
- Qwen3-VL-235B-A22B-Instruct-FP8 (235.7B, F8_E4M3)
- Qwen3-VL-235B-A22B-Instruct-FP8-dynamic (235.8B, F8_E4M3)
- Qwen3-VL-30B-A3B-Instruct-FP8 (31.1B, F8_E4M3)
- Qwen3-VL-30B-A3B-Instruct (31.1B, BF16)
- Qwen3-235B-A22B-Instruct-2507-FP8 (235.1B, F8_E4M3)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for image-text-to-text within about a third of Qwen3-VL-235B-A22B-Instruct's 235.7B parameters, from other model lines.
- GLM-5.3-Flash (321.3B, F8_E4M3)
- Inkling-Small (266.0B, BF16)
- step3 (321.0B, BF16)
- Intern-S1 (240.7B, BF16)
- Darwin-180B-RSI (180.0B, BF16)
More on Qwen3-VL-235B-A22B-Instruct
Fits on a 288 GB GPU at FP8: every model that fits in 288 GB.
Fits on a 141 GB GPU at INT4: every model that fits in 141 GB.
Best vision-language models: how Qwen3-VL-235B-A22B-Instruct ranks against the rest.
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
