DeepSeek open-sources DSpark and makes V4 generate 85% faster

DeepSeek just dropped DSpark, a speculative decoding framework that makes DeepSeek-V4 Flash 60 to 85 percent faster at token generation. V4 Pro gets 57 to 78 percent. No quality loss. The speedup is measured against their own single-token MTP baseline, and it is already live in production traffic.

This is not a new model. DeepSeek-V4-Pro-DSpark on HuggingFace is the same V4 Pro checkpoint with a speculative decoding module bolted on. The interesting part is the algorithm.

Two problems with existing speculative decoding

Speculative decoding works by having a small draft model guess multiple tokens in parallel, then the main model verifies them in one pass. Faster if enough guesses are right. Two things kill the speedup.

First: acceptance rate decay. Parallel draft models generate tokens that are individually plausible but mutually inconsistent. Token one is a good guess. Token eight, conditioned on guesses two through seven being slightly wrong, drifts badly. The further into the block, the worse it gets.

Second: wasted verification compute. Every draft token gets sent for verification, even the low-confidence tail ones that are almost certainly wrong. Under high concurrency, those doomed tokens eat batch capacity that could be serving other requests.

What DSpark actually does

DSpark attacks both problems.

For the decay problem, it adds a lightweight serial autoregressive module on top of the parallel generation. This models dependencies between tokens within a block without giving up the throughput advantage of parallel generation. The draft model still generates a block of tokens in parallel, but the serial module refines the internal coherence.

For the wasted compute problem, DSpark introduces a confidence head that estimates how likely each draft token is to survive verification. A hardware-aware prefix scheduler then dynamically decides how many tokens from the front of the draft to actually send for verification. Low-confidence tail tokens get cut. The scheduler runs asynchronously using an approach they call Zero-Overhead Scheduling, which borrows predictions from the previous two steps to decide the current truncation length without stalling the GPU pipeline.

The result is lossless. The output distribution of the target model is reconstructed exactly. No distillation, no approximation.

The benchmarks

Against Eagle3, the current autoregressive speculative decoding SOTA, DSpark improves average acceptance length by 26.7 to 30.9 percent on Qwen3 4B/8B/14B. Against DFlash, the parallel draft SOTA, it is 16.3 to 18.4 percent better.

Validation covered math reasoning (GSM8K, MATH500, AIME25), code generation (HumanEval, MBPP, LiveCodeBench), and dialogue (MT-Bench, Alpaca, Arena-Hard-V2). Across all of it, quality matches the base model.

DeepSpec: the open-source toolchain

Alongside the algorithm, DeepSeek open-sourced DeepSpec under MIT license. It is a full training and evaluation codebase for speculative decoding draft models. Three-stage workflow: data preparation, training, evaluation.

It supports three draft model algorithms out of the box: DSpark, DFlash, and Eagle3. Target models are currently Qwen3 and Gemma families. Training assumes a single node with 8 GPUs. The data preparation step requires regenerating target model answers and building a target cache, which is roughly 38 TB for the default Qwen3-4B setting.

The repo had 679 stars within hours of the commit going live.

The broader picture

The HN thread is full of people noting that American labs stopped publishing this kind of work. One commenter put it bluntly: DeepSeek pushes boundaries and publishes papers explaining how, something the US labs no longer do. Another replied that US labs are sitting on massive investment and revealing optimizations would erode their competitive position. A third pointed out that Chinese labs still have incentive to publish because they are behind, and their tune will change if they take the lead.

Whether that last point is cynical or realistic, the result is concrete. The code is out. The paper is out. The checkpoints are on HuggingFace. You can train your own DSpark draft model on Qwen3 today.

Over on r/unsloth, the thread hit 353 upvotes within hours. Daniel Hanchen from Unsloth posted it with the note that DSpark boosts throughput by 51 to 400 percent versus single MTP. People were already asking when it lands in vLLM.