DSpark
A speculative decoding algorithm released by DeepSeek, engineered specifically for the Multi-head Latent Attention (MLA) architecture with a dedicated draft strategy. It has been merged into the vLLM main branch and supports the Qwen3 and Gemma model series.
Timeline (last 90 days)
A DSpark configuration using 4 R9700-31K graphics cards reaches 55–69 tok/s running DeepSeek V4.1 Flash under Strata 0.2.4.
DSpark in DeepInfer first estimates the probability that a draft will pass, then, combined with hardware throughput, adjusts how long a draft each request is worth verifying.
The implementation, including expert scheduling and DSpark integration, has been open-sourced in llama.cpp-omni.
DSpark is a speculative decoding implementation that works in concert with LES's expert preloading to further amplify on-device throughput gains.
On the same benchmark with the Qwen3.8-27B model, DFlash2 achieves a 2.80x speedup, DSpark 2.57x, and MTP 2.22x.
DLite outperforms the current state-of-the-art method DSpark by up to 70% under agentic workloads.
Muse Spark Open includes a mechanism called DSpark, whose specific meaning is not explained in detail in the original information.
Meta provides two dedicated draft models for speculative decoding: DFlash V2 is designed for SGLang and is reportedly achieving a 3.7x throughput improvement on AMD MI355X hardware, accepting an average of 6.4 draft tokens per step; DSpark is designed for vLLM and proposes up to seven tokens per pass.
DSpark is a representative baseline method in the field of speculative decoding, belonging to one of the routes of joint optimization of the draft and the target model, and is used as a reference in JetSpec++'s comparison as the current state-of-the-art (SOTA) method.
JetSpec++ achieves a 1.54x wall-clock time improvement relative to DSpark.
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All Facts (18)
A DSpark configuration using 4 R9700-31K graphics cards reaches 55–69 tok/s running DeepSeek V4.1 Flash under Strata 0.2.4.
50%UnverifiedDSpark in DeepInfer first estimates the probability that a draft will pass, then, combined with hardware throughput, adjusts how long a draft each request is worth verifying.
50%UnverifiedDSpark is a speculative decoding implementation that works in concert with LES's expert preloading to further amplify on-device throughput gains.
50%UnverifiedOn the same benchmark with the Qwen3.8-27B model, DFlash2 achieves a 2.80x speedup, DSpark 2.57x, and MTP 2.22x.
50%UnverifiedDLite outperforms the current state-of-the-art method DSpark by up to 70% under agentic workloads.
50%UnverifiedMuse Spark Open includes a mechanism called DSpark, whose specific meaning is not explained in detail in the original information.
50%UnverifiedMeta provides two dedicated draft models for speculative decoding: DFlash V2 is designed for SGLang and is reportedly achieving a 3.7x throughput improvement on AMD MI355X hardware, accepting an average of 6.4 draft tokens per step; DSpark is designed for vLLM and proposes up to seven tokens per pass.
50%UnverifiedDSpark is a representative baseline method in the field of speculative decoding, belonging to one of the routes of joint optimization of the draft and the target model, and is used as a reference in JetSpec++'s comparison as the current state-of-the-art (SOTA) method.
50%UnverifiedJetSpec++ achieves a 1.54x wall-clock time improvement relative to DSpark.
50%UnverifiedCompared with the DSpark baseline, ReaDy improves on all benchmarks, with throughput increasing by an average of 7.97% and accepted length increasing by an average of 8.50%.
50%UnverifiedReaDy is built on top of DSpark's parallel backbone and sequence prediction head, using lightweight two-stage adaptation.
50%UnverifiedMiniCPM-V 4.7 35B-A3B uses a combination of three techniques—Lookahead Expert Scheduling (LES), fine-grained layer-by-layer quantization, and DSpark speculative sampling—to accelerate on-device inference.
50%UnverifiedLFM2-VL achieves a 1.66x to 2.56x speedup at batch 1 via DSpark speculative decoding.
50%UnverifiedDFlash and DSpark have made targeted optimizations in draft generation efficiency and hardware adaptation, and may outperform classic solutions under specific models and workloads.
50%UnverifiedvLLM v0.28.0 introduces DFlash2 and DSpark confidence-scheduled verification in the speculative decoding domain.
50%UnverifiedThe Q8 main model occupies 162 GB, preventing the drafter from fitting into unified memory, rendering DSpark acceleration ineffective; after moving three layers of MoE experts back to the CPU, output only improved from 22.87 to 23.49 tokens/second (2.7% faster), while prefill dropped from 95.38 to 76.65.
50%UnverifiedDeepSeek's official DSpark is essentially speculative decoding/multi-token prediction technology, requiring a drafter model and two sets of KV Cache resident in high-speed memory to achieve optimal results.
50%UnverifiedThe implementation, including expert scheduling and DSpark integration, has been open-sourced in llama.cpp-omni.
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