Tencent added Qwen3.8-27B support to DSpine and released an Apache-2.0 draft checkpoint for speculative decoding. The documented SGLang path supports one GPU, as many as eight concurrent requests and 131,072-token target context with workload-specific verification-budget tuning.
Why it matters: The release creates a downloadable acceleration path for a current 27B model rather than only a paper design. Teams can now test whether a small parallel drafter improves their own latency and throughput without changing target-model outputs.
SGLang 0.5.22 adds new model and accelerator paths, a 10x-faster InstantTensor loader claim, Apple Silicon support through PyTorch and multiple speculative-decoding optimizations. It also fixes a correctness issue where hybrid linear models could resume from the wrong recurrent state when structured output and speculative decoding were combined.
Why it matters: The release mixes throughput claims with a concrete correctness repair on a high-risk serving combination. Operators gain wider deployment options, but Python and runtime changes make a controlled upgrade more important than the headline performance numbers.