Show HN: Benchmarking DSpark speculative decoding on a consumer GPU (github.com)

A new benchmarking project has emerged showcasing the performance of DSpark speculative decoding on consumer GPUs, specifically for the popular LLM, llama.cpp. DSpark, developed by DeepSeek, enhances text generation speed by allowing a small draft model to propose multiple tokens at once, which are then verified by a larger model. This approach is significant for the AI/ML community as it demonstrates a practical application of speculative decoding that can substantially reduce latency without compromising output quality. Users can now directly contribute benchmarks from their own consumer GPUs, helping to establish a collaborative understanding of DSpark's efficacy in various computing environments. The benchmarks reveal that DSpark offers notable speed improvements: it achieved 133 tokens per second on an AMD Radeon RX 7900 using ROCm, marking a 2.25x speed increase over traditional decoding methods. Notably, the choice of draft length and backend significantly influences performance, with DSpark outperforming other speculative techniques across different setups. While prompt processing slows down by only 8-9%, the gain in generation speed varies by task, with coding tasks benefiting the most. Moreover, DSpark maintains good output fidelity, producing slightly varied results that are fluent but not identical to traditional methods, making it an attractive option for developers seeking faster results without sacrificing performance.