llamaperf

Qwen3.8 27B

on 4× NVIDIA RTX 5060 Ti 16GB · vLLM

Tone: positiveNVIDIA hardware
Oct 5, 2026
Throughput
2500-3000 tokens/s prompt processing (prefill)Prompt processing measures input prefill speed; it does not tell you the time to first token.
Quant
FP8 (FP8)
System RAM
128 GB
VRAM reported
76 GB

Use cases

codingagentic

Summary

User reports running Qwen3.8 27B with tensor parallelism across 4x RTX 5060 Ti 16GB in a Lenovo P620 (Threadripper PRO 3975WX, 128GB DDR4). Setup is vLLM with FP8 weights and MTP3 speculative decoding, headless Ubuntu, NCCL 2.30.7. Decode at C1 is 100-110 tokens/s and prefill is 2.5-3k tokens/s per stream. User compares NVFP4+MTP3 (137-141 t/s C1, 422 t/s C4 sum, ~3.5k prefill), SGLang NVFP4+DFlash (118-137 t/s C1, 357-386 t/s C4 sum, ~3.4k prefill), and llama.cpp Q6+MTP3 (95 t/s C1, 83 t/s C4 sum, ~1.2k prefill). NVFP4 was 1.4x faster than FP8 but degraded in MTP3 tests (F1 0.44 vs 0.73).