llamaperf

Qwen3.8 27B

on NVIDIA RTX 5080 · llama.cpp · 160,000 ctx

Tone: mixedNVIDIA hardware
Sep 22, 2026
Throughput
80.0 t/s gen · 1200.0 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_0
Flash Attention
on
System RAM
64 GB
VRAM reported
16 GB

Use cases

coding

Summary

User reports Qwen3.8 27B at 80+ t/s generation and 1200+ t/s prompt processing on an RTX 5080 with 16GB VRAM, using CUDA unified memory to fit the model. Setup is llama.cpp with the Unsloth UD Q3_K_XL GGUF, 160000 context, q8_0 KV cache, flash attention, and MTP speculative decoding with ngram-mod draft. User asks whether Q3 quants are good enough for harness coding with opencode, pi, and qwen-cli, and notes this is the only quant that fits the whole model in VRAM.