Can I run Qwen3.8-27B-OBLITERATED on an NVIDIA H100?

Yes

Its published weights (55.6 GB) fit in the 72.0 GB an NVIDIA H100 can use, with room for ≈ 243K tokens of context.

Full analysis of Qwen3.8-27B-OBLITERATED Everything that runs on NVIDIA H100 Get the weights on Hugging Face

hf download OBLITERATUS/Qwen3.8-27B-OBLITERATED

Files and context on NVIDIA H100

Each file of Qwen3.8-27B-OBLITERATED (and of GGUF versions the site has analyzed), and how much context fits next to it, with the context cache in 16-bit or 8-bit.

File	Size	On NVIDIA H100	Context (16-bit)	Context (8-bit)
As published	55.6 GB	Fits	243K	256K
4-bit (estimate)	15.6 GB	Fits	256K	256K

Qwen3.8-27B-OBLITERATED on other hardware

NVIDIA H100Yes
RTX 5090Probably, at 4-bit
RTX 4090Probably, at 4-bit
RTX 3090Probably, at 4-bit
RTX 5060 Ti 16 GBNo
RTX 3060 12 GBNo
RTX 4060No
Mac with 32 GBProbably, at 4-bit
Mac with 64 GBProbably, at 4-bit
Mac with 128 GBYes

Popular models that run on NVIDIA H100

Qwen3-0.6BQwen · 752M
Qwen3-VL-8B-InstructQwen · 8.77B
gpt2openai-community · 137M
tiny-Qwen2ForCausalLM-2.5trl-internal-testing · 2.44M
Qwen3-8BQwen · 8.19B
Qwen3-Coder-30B-A3B-Instruct-GGUFunsloth · 30.5B
gemma-4-26B-A4B-itgoogle · 25.8B
Qwen3.6-35B-A3B-FP8Qwen · 36.0B

Assumes 90% of the card's memory is usable and at least 4K tokens of context for one conversation, with the context cache in 16-bit unless noted. Fits leaves at least 20% of that memory for context; Tight leaves less.