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Qwen-3.6 27B model performance optimization discussed on Reddit

A user on Reddit's r/LocalLLaMA subreddit is seeking to optimize the performance of the Qwen-3.6 27B model running on a setup with three NVIDIA 2080 Ti GPUs. They are currently achieving 55 tokens per second using llama.cpp and have shared their specific configuration parameters, including model quantization (Q5_K_M), context size, and batch sizes, in hopes of receiving advice for further performance improvements. AI

IMPACT Niche discussion on optimizing inference speed for a specific model and hardware configuration.

RANK_REASON User-level discussion on optimizing a specific model and hardware setup using a particular software tool.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Qwen-3.6 27B model performance optimization discussed on Reddit

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/AccountGotLocked69 ·

    Qwen 3.6 27B Q5 on 3x2080ti: 55tps with llama.cpp. Can I squeeze out more?

    <!-- SC_OFF --><div class="md"><p>CPU: Threadripper 3970X<br /> RAM: 128GB DDR4<br /> GPUs: 3x2080ti 11GB</p> <p>The current best parameters to run it:</p> <pre><code>llama-server \ --model Qwen3.6-27B-Q5_K_S.gguf \ --n-gpu-layers 999 \ --split-mode tensor \ --flash-attn on \ --c…