LLM Reddit May 6, 2026 1 min read
A LocalLLaMA user has shared a detailed guide for running Qwen 3.6 27B with Multi-Token Prediction support in llama.cpp, achieving 2.5x inference speedup and 262k context on 48GB of memory.
LLM Inference Speedup: The Rise of Multi-Token Prediction
Current state
How Multi-Token Prediction is delivering 2-3x inference speed gains for local LLMs, from Qwen 3.6 27B to Gemma 4.
What changed recently
- Qwen 3.6 27B Achieves 2.5x Faster Local Inference via MTP With 262k Context on 48GB
- Google Releases Multi-Token Prediction Drafters for Gemma 4: Up to 3x Speedup
Key tensions
Optimistic case: LLM Inference Speedup: The Rise of Multi-Token Prediction unlocks real, compounding leverage.
Skeptical case: reliability, cost, and control around LLM Inference Speedup: The Rise of Multi-Token Prediction remain unresolved.
Signals to watch
- Momentum and new coverage around “inference”
- Momentum and new coverage around “mtp”
- Momentum and new coverage around “speculative-decoding”
Timeline
Latest
Recent development
LLM Reddit May 6, 2026 1 min read
Google has released Multi-Token Prediction (MTP) draft models for the Gemma 4 family, achieving up to 3x inference speedup through speculative decoding without any loss in output quality.