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, 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) |
|---|---|---|---|---|---|
| FP8 (native) | 219.6 GB | 263.5 GB | RTX 4090 | 6 | $2.64/hr |
| INT4 (quantized) | 109.8 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-FP8-dynamic at its published (F8_E4M3) precision: 6× RTX 4090, at $0.441/hr per GPU ($2.64/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: 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 RTX 4090, 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 RTX 4090, so 6 of them come to $2.64/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 RTX 4090 cards at $2.64/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 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)
Related reading: RTX 4090 pricing and specs, and The best GPUs for AI, ranked.
