Performance report UD-Q3_K_XL on RTX 5090 + 384GB DDR5: 4 t/s

#14
by SlavikF - opened

System:

  • Intel Xeon W5-3425 (12 cores / 24 threads)
  • 384GB DDR5-4800 RAM
  • Nvidia RTX 5090 32GB VRAM

Using UD-Q3_K_XL quants.

Here is the speed I'm getting:

prompt eval time =   85s /  1312 tokens (   65.28 ms per token,    15.32 tokens per second)
       eval time =  766s /  3225 tokens (  237.62 ms per token,     4.21 tokens per second)
      total time =  851s /  4537 tokens

Running llama.cpp in docker:

services:
  llama-router:
    image: ghcr.io/ggml-org/llama.cpp:server-cuda13-b10015
    container_name: router
    devices:
      - "nvidia.com/gpu=all"
    ports:
      - "8080:8080"
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    volumes:
      - /var/lib/docker/.cache:/root/.cache
      - ./models.ini:/app/models.ini:ro
    entrypoint: ["./llama-server"]
    command: >
      --models-max 1
      --models-preset ./models.ini
      --host 0.0.0.0  --port 8080
      --offline
      --tools all

my models.ini:

version = 1

[unsloth/GLM-5.2-GGUF:Q3_K_XL]
ctx-size=16384
temp=1.0
top-p=0.95
min-p=0.01

I tried to use MTP (default config: spec-type=draft-mtp), but that caused the generation speed to slow down to TG 1.4 t/s.

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