What GPU do I need to run mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic?
27.4B parameters, published in F8_E4M3. View on Hugging Face
Set up Qwen3.5-27B…lled-FP8-DynamicQwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic is published by mconcat on Hugging Face, with 71,804 downloads and 16 likes to date. It's a Qwen3_5ForConditionalGeneration 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) | 25.5 GB | 30.6 GB | RTX 4080 Super | 1 | $0.338/hr |
| INT4 (quantized) | 12.7 GB | 15.3 GB | RTX A5000 | 1 | $0.176/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 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic at its published (F8_E4M3) precision: 1× RTX 4080 Super, at $0.338/hr per GPU ($0.338/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic: common questions
Does Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic fit on a 32 GB GPU?
Yes. At FP8 (native) it needs 30.6 GB of VRAM, so a 32 GB card holds it with 1.4 GB to spare. A 24 GB card is not enough for it at FP8 (native).
Is Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic already quantized?
Yes. It is published in FP8, one byte per parameter, so the 30.6 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 15.3 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.
What is the least VRAM Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic can run in?
15.3 GB, at INT4 (quantized), which fits a 16 GB card, against 30.6 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX 4080 Super at $0.338/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More mconcat models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
