What GPU do I need to run Qwen/Qwen3-Next-80B-A3B-Instruct-FP8?
81.3B parameters, published in F8_E4M3. View on Hugging Face
Set up Qwen3-Next-…A3B-Instruct-FP8Qwen3-Next-80B-A3B-Instruct-FP8 is published by Qwen on Hugging Face, with 180,383 downloads and 90 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-Next-80B-A3B-Instruct-FP8 at its published (F8_E4M3) precision: 1× RTX PRO 6000 on simplepod, at $1.00/hr per GPU ($1.00/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3-Next-80B-A3B-Instruct-FP8: common questions
Can Qwen3-Next-80B-A3B-Instruct-FP8 run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 90.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 95.0 GB RTX PRO 6000 on simplepod at $1.00/hr.
Is Qwen3-Next-80B-A3B-Instruct-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 90.9 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 45.4 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-Next-80B-A3B-Instruct-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX PRO 6000 on simplepod at $1.00/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 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)
