DSpark draft model boosts Spark-X2.6-4B inference speed over 2x in published tests

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Developers have released DSpark, a 624-million-parameter BF16 draft model designed to accelerate the Spark-X2.6-4B language model through speculative decoding. The draft proposes up to seven tokens per pass, which the target model then verifies in parallel, cutting the number of sequential decoding steps. Published benchmarks report average BF16 throughput gains of 2.14x on an NVIDIA RTX 4090 with llama.cpp and 2.50x on an Apple M5 Pro with MLX across six datasets. The results depend on hardware, precision and workload, and they come from the model card rather than an independent evaluation. The draft is available on Hugging Face and ModelScope under the Apache 2.0 license, and integration into upstream llama.cpp is still in progress.

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