Tencent's DSpine Pushes Qwen3.8-27B to 104.8 tok/s on a Single GPU

A community llama.cpp port of Tencent’s DSpine speculative-decoding model reached 104.8 decode tokens per second with Qwen3.8-27B in the author’s single-GPU test. The same target model produced approximately 29 tok/s without speculation, giving DSpine a 3.6x advantage. It also outpaced the author’s DFlash2 result of 58.2 tok/s.

The release includes GGUF files converted from Tencent’s original checkpoint . The 2-billion-parameter drafter pairs with the full Qwen3.8-27B target and carries an Apache-2.0 license, which permits commercial use subject to the license terms.

How DSpine drafts 16 tokens

Speculative decoding reduces target-model work by asking a smaller model to propose several upcoming tokens. The target evaluates the proposed block in one forward pass and accepts the valid prefix. A higher acceptance rate spreads each expensive target pass across more generated tokens. With compatible sampling and verification, final token selection continues to follow the target model’s decoding rules.

DSpine builds on Tencent’s earlier DFlash and DFlash2 designs. Its five-layer draft network reads hidden states from five target-model layers and predicts a 16-token block in parallel. Three mechanisms help those parallel predictions retain information about preceding tokens:

• Causal block attention: Each draft position can attend only to earlier positions in the same block, preventing access to future tokens.
• Code bridges: After every layer, each position receives a gated message derived from its predecessor’s predicted token code. This carries causal information through the draft network’s depth.