#Reference build: 2× P100 on a Dell T5810 (YouTube)

A summary of the video description the owner pasted on 2026-11-01. It is the target configuration for Tersler. Its numbers are the video author's; they haven't been reproduced here yet.

#Reported results (2× P100, Qwen3.8 27B Q6_K, speculative decoding on)

Test	Result
Reasoning puzzle, reasoning effort "Low" (single run)	66.7 tok/s (Q4_K_M: 67)
~4,000-token document prompt (median of 3)	49.2 tok/s
Speculative decoding off	29.7 tok/s
Prompt processing	~400 tok/s
128K context full (thinking off)	37 tok/s
Gemma 4 26B-A4B IQ4_XS, one card	79 tok/s
RTX 3090, same speculative decoding	66.4 tok/s

The author reports a later improvement to 74 tok/s (announced as the next video).

#Their platform

Dell Precision T5810, Xeon E5-2690 v4, 80 GB RAM.
A second PSU just for card 2.
Ducted shroud.

#Recipe

#Cards

Back up the firmware: nvflash --save p100_cardN_backup.rom.
Graphics mode, because the T5810 BIOS can't map 16 GB BAR1: nvflash -i N --gpumode graphics.
- The first reboot may power-cycle three times.
- Re-enable ECC afterwards: nvidia-smi -i N -e 1 + reboot.
- On Tersler, try Above 4G Decoding first; see hardware-notes.md.
Power cap: nvidia-smi -pm 1, then nvidia-smi -i N -pl 150. More power gave < 1% more speed.

#System

Ubuntu 24.04.
NVIDIA driver 580 (server branch, the last one with Pascal support).
CUDA 12.9.
Kernel boot parameter iommu=pt, needed for P2P (GGML_CUDA_P2P=1). Without it, P2P crashed both cards. Without iommu=pt, leave P2P off; it's worth only a few percent.

#Software

Kmic-68's P100 fork: https://github.com/Kmic-68/llama.cpp, branch p100-optimizations, tested at commit d90ec36a3.
Build: -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=60 -DGGML_CUDA_NCCL=OFF -DGGML_CUDA_FA_QUANTS=all. sm_60 only: a second architecture disables the Pascal tuning. See the repo's p100-docs/BUILD.md.

#Model

Qwen3.8-27B Q6_K GGUF including the MTP head. The author ran a community finetune of the same size (Qwen3.8-27B-Uncensored-Q6_K.gguf).

#Launch

GGML_CUDA_P2P=1 GGML_CUDA_GRAPHS_PRE_VOLTA=3 LLAMA_SPEC_SAMPLE_TEMP=1.0 LLAMA_SPEC_DRAFT_TOPK=20 \
llama-server -m Qwen3.8-27B-Uncensored-Q6_K.gguf -ngl 99 -sm tensor -fa 1 -ctk q4_0 -ctv q4_0 \
  -c 131072 -b 32768 -ub 2048 -np 1 \
  --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.2 -ngld 99 -ubd 64 -ctkd q4_0 -ctvd q4_0 \
  --jinja --temp 1.0 --top-k 20 --top-p 0.95 --min-p 0.0

Their notes on the command:

Drop GGML_CUDA_P2P=1 without iommu=pt.
Two drafts vs four: about the same.
One card at PCIe x8: ~2% slower.

Kmic: https://github.com/Kmic-68/llama.cpp
fallentree GP100 fork: https://github.com/thefallentree/llama.cpp (URL truncated in the description; verify)
qr0n P100 patches: https://github.com/key-sh/p100-llama-cpp
Upstream: https://github.com/ggml-org/llama.cpp