LM Studio 0.4.14+ now implements Multi‑Token Prediction (MTP) speculative decoding, eliminating the need for a separate draft model and delivering roughly double the token throughput—e.g., Qwen3.6‑35B reaches about 130 tokens/s on RTX 3090—while providing a six‑step activation guide and a list of known pitfalls.
The article explains how to enable Multi‑Token Prediction (MTP) in Qwen3.6 using a specific llama.cpp PR, achieving up to 1.5× faster local inference, details compilation steps, optimal parameters, memory requirements, and how to integrate the accelerated model with Claude Code while avoiding common pitfalls.
Unsloth adds MTP to Qwen3.6‑27B and 35B‑A3B models, delivering 1.5‑2× decoding speed gains on consumer‑grade GPUs, with ~80% draft acceptance, while providing installation steps, usage parameters, benchmark results, and guidance on suitable scenarios.
Alibaba has added four new open‑source weight versions to its Qwen3.6 series, featuring the 27‑billion‑parameter dense multimodal model Qwen3.6‑27B and the 35‑billion‑parameter sparse expert model Qwen3.6‑35B‑A3B, both designed for stable, real‑world coding tasks and outperforming their Qwen3.5 predecessors.
Qwen3.6-35B-A3B, the first open‑source Qwen3.6 model, achieves markedly better scores than Qwen3.5‑35B‑A3B and Gemma4‑31B on Terminal‑Bench2.0, NL2Repo, and QwenClawBench, adds a thought‑process retention option, and is accessible via HyperAI’s ready‑to‑run notebook with free compute credits.
The article details how to deploy the 4‑bit quantized Qwen3.6-35B model with vLLM 0.17 (and 0.19.1 patch) on a Docker container, compares its memory usage and token‑generation speed to Qwen3.5‑35B, and shares practical scripts and observed performance of roughly 150 tokens per second.
The article reviews three optimization paths for the Qwen3.6‑35B model—four‑bit AWQ quantization variants, the DFlash speculative decoding accelerator, and a Claude Opus‑based distillation—detailing their implementation steps, benchmark results, and guidance on selecting the best version for different hardware and performance needs.
Qwen3.6‑35B‑A3B, a mixture‑of‑experts model that activates only 3 B parameters, outperforms leading AI systems across SWE‑bench, Terminal‑Bench, NL2Repo and several agentic coding benchmarks, while also achieving top scores in GPQA, HMMT and RealWorldQA, prompting a reassessment of domestic LLM capabilities.
