llamaperf

Qwen3.8 27B

on NVIDIA RTX 5070 Ti · beellama.cpp · 96,256 ctx

Tone: positiveNVIDIA hardware
Sep 6, 2026
Throughput
75.0 t/s gen · 1700.0 tokens/s prompt processing (prefill)Prompt processing measures input prefill speed; it does not tell you the time to first token.
Quant
Q3_K_XL (GGUF)
KV cache
Q8
VRAM reported
16 GB

Summary

User reports Qwen3.8-27B-UD-Q3_K_XL.gguf at up to 75 t/s tg and up to 1700 t/s pp on an RTX 5070 Ti 16GB, fully offloaded, with vision on CPU. Setup is beellama.cpp with kvarn performance enhancements, MTP n-max=2, KV=kvarn3/kvarn3, MTP draft KV=kvarn2/kvarn2, 96256 context and 1024 tail tokens. The user notes Q3_K_XL scores almost 100% on a needle test with kvarn3, while Q3_XXS does poorly at 84% with F16 KV and ~80% with Q3 KV, and that kvarn is better with KLD. The user advises not fearing Q3 quants or KV quantisation, and concludes that for 16GB VRAM Q3_K_XL with kvarn3 works well.