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Can I run Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 on an NVIDIA B300?

Yes

Its Q8_0 file (29.1 GB) fits in the 259 GB an NVIDIA B300 can use, with room for ≈ 256K tokens of context.

Full analysis of Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 Everything that runs on NVIDIA B300 Get the Q8_0 file on Hugging Face
ollama run hf.co/nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2:Q8_0

Files and context on NVIDIA B300

Each file of Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 (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.

FileSizeOn NVIDIA B300 Context (16-bit)Context (8-bit)
Q8_029.1 GBFits256K256K
Q6_K23.2 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-Q8MTP19.8 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q4LynnStyle-MTP19.6 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-Q8MTP17.5 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q3LynnStyle-MTP17.3 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-Q8MTP13.5 GBFits256K256K
Qwen3.8-27B-Coder390-Q3LynnStyle16G-MTP13.3 GBFits256K256K
Qwen3.8-27B-Coder390-EfficientThink-Q2LynnStyle-MTP13.3 GBFits256K256K

Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2 on other hardware

Popular models that run on NVIDIA B300

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.