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.
