llamaperf

Qwen3.8 27B

on NVIDIA RTX 3090 · llama.cpp · 150,000 ctx

Tone: mixedNVIDIA hardware
Sep 7, 2026
Throughput
45-50 t/s gen
Quant
Q4_K_XL (GGUF)
KV cache
Q8
MTP (Multi-Token Prediction)
on
VRAM reported
24 GB

Use cases

codinglong-context

Summary

User compares llama.cpp with a Q4_K_XL quant against vLLM with W4A16-AutoRound on an RTX 3090. llama.cpp reaches about 45-50 t/s at 150K context, while vLLM reaches about 55-65 t/s at 175K context. The user prefers vLLM for speed but notes llama.cpp is needed for vision, and asks about the quality difference between the quants for coding.