llamaperf

Qwen3.6 35B (3B active)

on NVIDIA RTX 5050 Laptop 8GB · llama.cpp · 90,000 ctx

Tone: positiveNVIDIA hardware
Nov 1, 2026
Throughput
40-46 t/s gen · 970-1000 tokens/s prompt processing (prefill)Prompt processing measures input prefill speed; it does not tell you the time to first token.
Quant
Q4_K_XL (GGUF)
KV cache
q4_0
System RAM
32 GB
VRAM reported
8 GB

Use cases

codingagentic

Summary

User reports Qwen3.6-35B-A3B on an RTX 5050 Laptop 8GB at ~970-1000 tok/s prefill and ~40-46 tok/s decode on a 21.8k-token Python prompt generating 400 tokens. Setup is a llama.cpp fork with a hybrid MoE expert cache (800 MiB), Q4_K_XL GGUF, q4_0 KV cache, 90000 context, ubatch 4096, n-cpu-moe 40 and MTP draft speculation; experts ran from system RAM on a 32 GB DDR5 laptop. The baseline config (n-cpu-moe 34, ubatch 1024, no MoE cache) gave ~480 tok/s prefill and 41-43 tok/s decode, so the main gain is roughly 2x prefill. The user also tried the Moex engine at ~59-63 tok/s decode and ~826 tok/s prefill, and their branch reached 57-62 tok/s decode at 252K context.