DSpark Lifts Qwen3-8B Inference 31.5% to 124.9 Tokens/s on the Same GPU
Updated
Updated · KDnuggets · Aug 31
DSpark Lifts Qwen3-8B Inference 31.5% to 124.9 Tokens/s on the Same GPU
1 articles · Updated · KDnuggets · Aug 31
Summary
124.9 tokens per second generation speed was recorded with DSpark enabled in llama.cpp, up from a 95.0 tokens/s baseline for Qwen3-8B on the same GPU.
DSpark achieved the gain by pairing a parallel draft model with a lightweight sequential component, letting later draft tokens use earlier predictions while dropping low-confidence candidates before verification.
The test used Qwen3-8B-Q4_K_M as the target model and a 1.2GB DSpark draft model, with identical prompt, 512-token limit and deterministic decoding settings to keep the comparison apples-to-apples.
Prompt processing fell to 88.0 tokens/s from 294.6 tokens/s, but the report says faster autoregressive generation matters more for longer outputs where inference time is dominated by token generation.
DeepSeek has reported 60% to 85% gains versus its MTP-1 production baseline in DeepSeek-V4, though the guide says local users still face limited model support and newer, less mature llama.cpp integration.
DSpark boosts generation speed by 31 percent, but slows prompt processing. When does this highly-praised local AI upgrade actually become a downgrade?
With native multi-token prediction built directly into newer models, are separate speculative draft models like DSpark already destined for obsolescence?