A Reddit post discusses the cost-effective hardware setup for running Qwen 3.6 models, both 27B and 35B-A3B, noting that RTX 3090 24GB offers better long-term value over Tesla V100 due to discontinuation and upcoming Chinese alternatives. The proposed build totals $1,995.65, including a Ryzen 5 5600X, RTX 3090 24GB, and essential components, with the total price being a key concern for users seeking affordability.
Cheapest hardware for Qwen 3.6: 27B and 35B-A3B models
qwen4exp adds recurrent state rollback support for MTP speculative decoding
The qwen4exp project has implemented recurrent state rollback support to enable effective Multi-Token Prediction (MTP) speculative decoding. This change allows the target state to move back by the number of rejected draft tokens, preventing unnecessary serialization of the entire recurrent state to host memory.
Qwen3.6 35B-A3B generates flight simulator in single prompt
A Reddit user demonstrated Qwen3.6 35B-A3B generating a complete, relaxing flight simulator with mountains, clouds, and endless procedural terrain from a single prompt.
Qwen 27B local performance on consumer hardware
A user reports that Qwen 27B, quantized to q6kxl and running with multi-token prediction on a system with 4090 and 3090 GPUs, achieves decode speeds of 50-90 tokens/s and pre-fill speeds of 1500-2200 token/s. The model reliably interfaces with various APIs and generates functional code for single-page apps, LaTeX docs, parsers, and crawlers.
Best Settings for 48GB VRAM with Qwen 3.6 27B
A user shares optimized settings for running Qwen 3.6 27B with Q8_0 quantization on an RTX 4090 and RTX 3090 setup using llama.cpp. The configuration includes tensor split, 999 layers on GPU, 250k context, speculative decoding, and unified KV cache, achieving 75-100t/s throughput with vision and MTP support.
$1800 GPU cost runs Qwen3.6-27B with 262K context and 55 tok/s
A setup using four 5060 Ti GPUs (totaling $1800) achieves 55 tokens per second with Qwen3.6-27B-FP8, supporting 262K context length and bfloat16 KV cache. The configuration uses P2P and FlashInfer, with benchmark results showing 55.67 output token throughput and 65.25% speculative decoding acceptance rate.