What GPU do I need to run RedHatAI/Qwen3-VL-235B-A22B-Instruct-FP8-dynamic?
235.8B parameters, published in F8_E4M3. View on Hugging Face
Set up Qwen3-VL-23…ruct-FP8-dynamicQwen3-VL-235B-A22B-Instruct-FP8-dynamic is published by RedHatAI on Hugging Face, with 29,042 downloads and 4 likes to date. It's a Qwen3VLMoeForConditionalGeneration model built for text-generation, published natively in F8_E4M3.
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) |
|---|---|---|---|---|---|
| FP8 (native) | 219.6 GB | 263.5 GB | L40 | 6 | $4.55/hr |
| INT4 (quantized) | 109.8 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-FP8-dynamic at its published (F8_E4M3) precision: 6× L40, at $0.759/hr per GPU ($4.55/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-FP8-dynamic: 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 (RedHatAI/Qwen3-VL-235B-A22B-Instruct-FP8-dynamic), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Qwen3-VL-235B-A22B-Instruct-FP8-dynamic: common questions
Can Qwen3-VL-235B-A22B-Instruct-FP8-dynamic run on a single GPU?
No. At FP8 (native) it needs 263.5 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB L40, and it takes 6 of them.
Is Qwen3-VL-235B-A22B-Instruct-FP8-dynamic already quantized?
Yes. It is published in FP8, one byte per parameter, so the 263.5 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 131.7 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run Qwen3-VL-235B-A22B-Instruct-FP8-dynamic?
6 at FP8 (native). It needs 263.5 GB of VRAM and the cheapest capable live offer is a 48.0 GB L40, so 6 of them come to $4.55/hr in total.
Does quantizing Qwen3-VL-235B-A22B-Instruct-FP8-dynamic lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is 6 L40 cards at $4.55/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 (235.7B, BF16)
- 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
Based on Qwen3-VL-235B-A22B-Instruct.
Alternatives at this size
Other models for text-generation within about a third of Qwen3-VL-235B-A22B-Instruct-FP8-dynamic's 235.8B parameters, from other model lines.
- MiniMax-M2.7 (228.7B, F8_E4M3)
- MiMo-V2.5 (310.8B, F8_E4M3)
- Step-3.5-Flash (199.4B, BF16)
- Hy3-preview (298.8B, BF16)
- DeepSeek-V2 (235.7B, BF16)
More on Qwen3-VL-235B-A22B-Instruct-FP8-dynamic
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.
Related reading: L40 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.
