Qwen3.6-27B-MTPLX-Optimized-Speed-V2

Qwen3.6-27B MTPLX Optimized Speed V2

The recommended 27B model for local coding and agent work with MTPLX on Apple Silicon.

V2 is a hand-calibrated quantization of Qwen3.6-27B built for one job: fast, high-quality coding on a Mac. It cuts quantization loss by 47% compared to the original Optimized Speed release, and it pulls further ahead the longer an agent session runs.

Quickstart

brew install youssofal/mtplx/mtplx
mtplx start

mtplx start recommends this model automatically on Macs with 32 GB of unified memory or more, and offers smaller verified models below that.

Direct usage:

mtplx pull Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed-V2
mtplx run "hello" --model Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed-V2
mtplx serve --model Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed-V2 --port 8000

mtplx serve exposes OpenAI and Anthropic compatible endpoints, so the model works inside OpenCode, Claude Code, Cline, and other coding agents.

The numbers

Measured on an M5 Max against the original Optimized Speed release, same engine, same prompts:

• KL divergence to the full-precision bf16 model: 0.072 vs 0.135, on an 86k-token held-out battery. 47% less quantization loss than the original.
• Draft acceptance at 17k tokens of agent context: 98 / 86 / 78% per draft position, vs 73 / 50 / 33% for the original.
• Decode at that context: 45.7 vs 36.1 tok/s, about 27% faster.
• Peak speculative multiplier: 2.86x over the matched autoregressive baseline, the highest we have measured on any 27B build.

These numbers are one story, not four. Speculative decoding is only as fast as its drafts are accurate, and a trunk that stays this close to full precision keeps its drafts landing deep into long contexts. Quality is the speed lever.

The tradeoff: V2 is 19.9 GB vs 16.4 GB for the original, so cold short chats can be a few percent slower. Everything else is faster.

How it is built

Not a round-to-nearest conversion. The layout was tuned tensor by tensor against the bf16 reference:

• Most weights are 4-bit with small groups, refit against real activation statistics with error-weighted least squares instead of naive rounding.
• The tensors that hurt most at 4-bit were promoted by hand: embeddings, the output head, convolution and attention output projections, and the late MLP layers run at 8-bit, with the most sensitive layers kept in full bf16.
• The native multi-token-prediction head ships as a calibrated sidecar (mtp/weights.safetensors). No external draft model is needed.

The result lands at roughly 5.8 bits per weight. The goal was never the smallest file. It is the best coding model that still fits comfortably in unified memory.

About MTPLX

MTPLX is an MLX-native inference engine built around Qwen's native multi-token prediction: the model drafts its own tokens and the engine verifies them with exact probability-ratio acceptance, so output quality matches non-speculative decoding at any temperature. It adds warm session caching for fast agent round-trips, a native macOS app, and a one-line install via Homebrew or pip install mtplx.

Which model to pick

• Coding, tool use, long agent sessions: this model.
• Tight on storage, mostly short chat: Qwen3.6-27B-MTPLX-Optimized-Speed is 3.5 GB smaller.
• Lower-memory Macs: Qwen3.5-9B and Qwen3.5-4B are verified for smaller memory tiers.

Verification

mtplx_runtime.json records the verified runtime contract:

• architecture: qwen3-next-mtp, maximum MTP depth 3, turbo profile
• sampler: temperature 0.6, top-p 0.95, top-k 20
• KLD and perplexity measured against the bf16 teacher on an 86k-token held-out battery
• verified hardware: Apple M5 Max, 128 GB unified memory
• served model id: mtplx-qwen36-27b-optimized-speed-v2

Files

• model-*.safetensors: calibrated hybrid-quantized model shards
• mtp/weights.safetensors: native MTP sidecar
• mtplx_runtime.json: verified MTPLX runtime contract
• tokenizer and processor files from the Qwen model family

Sources and attribution