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DeepSeek partners with Peking University to open-source DSpark, an inference acceleration tech boosting single-user speed by 57%-85% under high concurrency. Learn its three core designs and the DSpec framework.

DeepSeek and Peking University open-source DSpark, an inference acceleration tech boosting single-user generation speed by 57%-85% under high concurrency. Learn its 3 core designs and the DSpec framework.

DeepSeek and Peking University open-source DSpark, an inference acceleration technology using semi-autoregressive architecture and dynamic scheduling to boost LLM speed by 50%+ and double GPU concurrency without quality loss.

DeepSeek open-sources DSpark, delivering 50%–400% LLM inference speedup with no retraining or quantization, via semi-autoregressive drafting and confidence-scheduled verification.

SGLang officially integrates DSpark, solving the core pain point of speculative decoding failure under high-concurrency batches via confidence-driven variable-length verification. Supports Qwen3 and DeepSeek-V4, hitting 383.7 tok/s on B300.

Deploy DeepSeek-V4-Flash DSpark on 8× H20-141G using GPUStack's SGLang backend on Day 0. Full walkthrough of Web UI config, parameter tuning, and 200 tokens/sec benchmark results.

DeepSeek's speculative decoding algorithm (DSpark) is now merged into vLLM main branch, natively supporting Qwen3 and Gemma. Tests show ~150× single-user token speed gains and ~40–50% throughput improvement.

This week in AI: OpenAI launches GPT-5.6 in three tiers (Sol/Terra/Luna) hitting 91.9% on coding benchmarks; DeepSeek and PKU open-source DSpark for 85% faster inference; Prime Intellect trains trillion-param models on just 28 H200s; Anthropic Claude enters Slack.