What GPU do I need to run RedHatAI/Qwen3-Coder-Next-FP8-dynamic?
79.8B parameters, published in F8_E4M3. View on Hugging Face
Set up Qwen3-Coder-Next-FP8-dynamicQwen3-Coder-Next-FP8-dynamic is published by RedHatAI on Hugging Face, with 40,981 downloads and 2 likes to date. It's a Qwen3NextForCausalLM 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.
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-Coder-Next-FP8-dynamic at its published (F8_E4M3) precision: 1× RTX PRO 6000 on vastai, at $1.34/hr per GPU ($1.34/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3-Coder-Next-FP8-dynamic: common questions
Can Qwen3-Coder-Next-FP8-dynamic run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 89.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 96.0 GB RTX PRO 6000 on vastai at $1.34/hr.
Is Qwen3-Coder-Next-FP8-dynamic already quantized?
Yes. It is published in FP8, one byte per parameter, so the 89.1 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 44.6 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.
Does quantizing Qwen3-Coder-Next-FP8-dynamic lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX PRO 6000 on vastai at $1.34/hr. At INT4 (quantized) it drops to one RTX 8000 on akash at $0.221/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More RedHatAI models
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- gemma-4-12B-it-FP8-Dynamic (13.0B, F8_E4M3)
- Llama-3.2-1B-Instruct-FP8-dynamic (1.5B, F8_E4M3)
- Qwen3.5-9B-FP8-dynamic (9.4B, F8_E4M3)
- gemma-4-31B-it-FP8-dynamic (31.3B, F8_E4M3)
