This repository provides optimized serving configurations for the Qwen3.8-27B model running on a single 24 GB consumer GPU (RTX 3090). It achieves high throughput of approximately 1,000 tok/s at 64 concurrent users in batch mode and up to ~133 tok/s for single-user scenarios using speculative decoding techniques like MTP or DFlash2. The project includes custom patches, requantization scripts (such as int8 tensor-core GEMMs), and Docker support to enable extended context windows of 150k to 262k tokens on a single consumer card.
- Offers two distinct serving profiles: `batch` for high throughput/API backends and `single-user` for low-latency chat experiences.
- Implements advanced speculative decoding modes including MTP (Multi-Token Prediction) and DFlash2 block drafting.
- Supports extreme context lengths up to 262k tokens through KVarN, which utilizes a lossy 4/2-bit KV cache.
- Includes specialized optimizations like int8 activations, quantized lm_head, and split-KV verify attention to maximize VRAM efficiency.
- Offers two distinct serving profiles: `batch` for high throughput/API backends and `single-user` for low-latency chat experiences.
- Implements advanced speculative decoding modes including MTP (Multi-Token Prediction) and DFlash2 block drafting.
- Supports extreme context lengths up to 262k tokens through KVarN, which utilizes a lossy 4/2-bit KV cache.
- Includes specialized optimizations like int8 activations, quantized lm_head, and split-KV verify attention to maximize VRAM efficiency.
This guide explains how to implement Multi-Token Prediction (MTP) models, such as Gemma 4 and Qwen3.6, to increase inference speeds on local hardware. By predicting multiple tokens at once rather than one per step, MTP can achieve speedups of approximately 1.4x to 2.2x when using GGUF files without losing accuracy. The guide covers requirements for VRAM headroom, specific implementations for Gemma 4 and Qwen models, and provides setup instructions for both Unsloth Studio and llama.cpp environments.
- Accelerates inference through multi-token prediction
- Compatible with Gemma 4 and Qwen3.6/3.5 models
- Supported in Unsloth Studio and llama.cpp
- Accelerates inference through multi-token prediction
- Compatible with Gemma 4 and Qwen3.6/3.5 models
- Supported in Unsloth Studio and llama.cpp
This guide provides instructions for running Alibaba's Qwen3.6 multimodal hybrid-thinking models locally using Unsloth tools. It covers the 27B and 35B-A3B variants, which support a 256K context window across 201 languages and excel in agentic coding, vision, and chat tasks. The article details hardware requirements for various quantization levels and explains how to leverage Multi Token Prediction (MTP) for significantly faster inference.
Key topics:
- Hardware memory requirements for quantized models
- Faster generation via Multi Token Prediction (MTP)
- Integration with Unsloth Studio, llama.cpp, and MLX
- Preserved thinking mode configurations
Key topics:
- Hardware memory requirements for quantized models
- Faster generation via Multi Token Prediction (MTP)
- Integration with Unsloth Studio, llama.cpp, and MLX
- Preserved thinking mode configurations
Google has released Multi-Token Prediction (MTP) drafters for the Gemma 4 model family to significantly accelerate inference speeds. By utilizing a specialized speculative decoding architecture, these drafters can deliver up to a 3x speedup without compromising output quality or reasoning capabilities. This technology addresses memory-bandwidth bottlenecks by allowing a lightweight drafter to predict multiple future tokens that are then verified in parallel by the larger target model.
Key points:
* Improved responsiveness for real-time chat, voice applications, and agentic workflows.
* Faster local development on personal computers and consumer GPUs.
* Enhanced performance and battery efficiency on edge devices.
* Architectural optimizations including KV cache sharing and activation utilization.
* Available now under the Apache 2.0 license via Hugging Face and Kaggle.
Key points:
* Improved responsiveness for real-time chat, voice applications, and agentic workflows.
* Faster local development on personal computers and consumer GPUs.
* Enhanced performance and battery efficiency on edge devices.
* Architectural optimizations including KV cache sharing and activation utilization.
* Available now under the Apache 2.0 license via Hugging Face and Kaggle.
