27b

vLLM Qwen 3.6-27B (AWQ-INT4) + DFlash speculative decoding on AMD Strix Halo (gfx1151 iGPU, 128 GB UMA, ROCm 7.13). 24.8 t/s single-stream, vision, tool calling, 256K context, OpenAI-compatible, Docker. Matches DGX Spark FP8+DFlash+MTP at a third of the cost. No CUDA.

Run Qwen 3.6-27B AWQ-INT4 models with DFlash speculative decoding on AMD Strix Halo hardware using vLLM for high-throughput inference.

Wire the 1CatAI Split-D D256 FlashAttention kernel (fishlikeX/sm70-attn, MIT) into NInfer on Tesla V100 sm_70: +36-41% prefill, TTFT -3min, decode unchanged. Measured data + integration guide. Published by the user with AI assistance.

Qwen3

27B Qwen3.8-class uncensored ternary model on a 16GB GPU: 256K context + MTP + vision | Qwen3.8 系 27B 无审查三值量化 · 256K 上下文 · 16GB 显卡实测 | RX 6900 XT (gfx1030)

This document summarizes the performance of Qwen 3

The Qwen 3

What is Qwen3

Qwen 3

In the realm of artificial intelligence, Swift Qwen, an open-source library for natural language processing (NLP), has just undergone a significant upgrade