Can I run it?

Can I run Cyber Tiel Coder 35B A3B GGUF MTP on NVIDIA H100 SXM 80GB?

Runs great

Runs great fully on your NVIDIA H100 SXM 80GB at Q8_0, 8K context, about 549–749 tok/s. Weights 35.1 GB plus 0.19 GB of KV cache — 36.0 GB of 76.0 GB usable. Up to 256K context still fits.

Best quantQ8_0 est.

Working context8K

Max that fits256K

Speed549–749 tok/s

Weights35.1 GB

KV cache0.19 GB

Total36.0 GB

Usable76.0 GB

Cyber Tiel Coder 35B A3B GGUF MTP details · NVIDIA H100 SXM 80GB page · How this estimate is made

Run commands

llama.cpp

# Quant size is estimated — the file may not be published yet
llama-server -hf peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTP:Q8_0 -c 8192 -ngl 40

No matching name in the Ollama library snapshot.

LM Studio

# Quant size is estimated — the file may not be published yet
lms get peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTP
# Then load quant Q8_0

Assumptions

Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
Q8_0 size is estimated from parameters × bits-per-weight because a GGUF file size was not available.
CPU runs keep about 4 GB plus a 10% cushion for the OS.
Speed is quoted at 8K context, not at the maximum context that still fits.

Larger models that also fit

Can I run Qwen3.8 35B A3B Distill on NVIDIA H100 SXM 80GB? — Tight fit, BF16, 298–407 tok/s
Can I run Qwen3.8 Flash Next on NVIDIA H100 SXM 80GB? — Tight fit, UD-IQ1_M, 144–196 tok/s
Can I run Qwen3 Coder Next on NVIDIA H100 SXM 80GB? — Tight fit, UD-Q6_K_XL, 140–190 tok/s

Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.