Model Anatomy See what's inside open-weight AI models

Can I run MiniCPM-V-4.7-35B-A3B-gguf on an NVIDIA H100?

Yes, just

Its F16 file (69.4 GB) fits in the 72.0 GB an NVIDIA H100 can use, with room for ≈ 122K tokens of context.

Full analysis of MiniCPM-V-4.7-35B-A3B-gguf Everything that runs on NVIDIA H100 Get the F16 file on Hugging Face
ollama run hf.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf:F16

Files and context on NVIDIA H100

Each file of MiniCPM-V-4.7-35B-A3B-gguf (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 H100 Context (16-bit)Context (8-bit)
F1669.4 GBTight122K244K
MiniCPM-V-4_7-35B-A3B-F16-thinking69.4 GBTight122K244K
Q8_036.9 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q8_0-thinking36.9 GBFits256K256K
Q6_K28.5 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q6_K-thinking28.5 GBFits256K256K
Q5_126.1 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q5_1-thinking26.1 GBFits256K256K
Q5_K_M24.7 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q5_K_M-thinking24.7 GBFits256K256K
Q5_024.0 GBFits256K256K
Q5_K_S24.0 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q5_0-thinking24.0 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q5_K_S-thinking24.0 GBFits256K256K
Q4_121.8 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q4_1-thinking21.8 GBFits256K256K
Q4_K_M21.2 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q4_K_M-thinking21.2 GBFits256K256K
Q4_K_S19.9 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q4_K_S-thinking19.9 GBFits256K256K
IQ4_NL19.8 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-IQ4_NL-thinking19.8 GBFits256K256K
Q4_019.7 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-Q4_0-thinking19.7 GBFits256K256K
IQ4_XS18.7 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-IQ4_XS-thinking18.7 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-mix_q3gud_im15.5 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-mix_q3gud_im-thinking15.5 GBFits256K256K
IQ3_M15.4 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-IQ3_M-thinking15.4 GBFits256K256K
IQ3_XS14.5 GBFits256K256K
MiniCPM-V-4_7-35B-A3B-IQ3_XS-thinking14.5 GBFits256K256K

MiniCPM-V-4.7-35B-A3B-gguf on other hardware

Popular models that run on NVIDIA H100

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.