The DSpark system from DeepSeek combines batch decoding, speculative decoding, draft‑model tricks, Eagle‑MTP, DFlash parallelism, variable‑length scheduling and online confidence calibration to deliver up to 85% speedup and four‑fold throughput gains while maintaining generation quality.
Xiaomi’s MiMo‑V2.5‑Pro UltraSpeed delivers a 1‑trillion‑parameter model that generates over 1000 tokens per second on a standard 8‑GPU server by combining FP4 quantization, MoE architecture, DFlash decoding and TileRT’s custom execution engine, challenging the need for dedicated ASICs.
The article explains how Xiaomi’s MiMo‑V2.5‑Pro‑UltraSpeed mode achieves a record‑breaking 1000 tokens per second inference speed, why such ultra‑fast performance matters for real‑time AI applications, and the FP4 quantization, DFlash decoding and TileRT inference technologies that make it possible without sacrificing model quality.
Xiaomi’s MiMo‑V2.5‑Pro UltraSpeed mode breaks the 1000 tokens‑per‑second barrier for a 1‑trillion‑parameter model by combining FP4 expert‑only quantization, DFlash block‑masked speculative decoding, and TileRT’s ultra‑low‑latency GPU system, and the API is now available through a limited‑time trial.
DFlash replaces the speculative draft model with a block‑diffusion drafter, generating 16 tokens per forward pass and achieving up to 6× speedup over baseline (2.5× over EAGLE‑3) without quality loss, while supporting a wide range of open‑source LLMs and multiple back‑ends.
The article reviews three optimization paths for the Qwen3.6‑35B model—four‑bit AWQ quantization variants, the DFlash speculative decoding accelerator, and a Claude Opus‑based distillation—detailing their implementation steps, benchmark results, and guidance on selecting the best version for different hardware and performance needs.
The article explains how DFlash’s block‑diffusion draft model and KV Injection boost speculative decoding speed by 5‑8× without sacrificing output quality, and how DDTree further raises the gain to over 8×, backed by benchmark results and integration guides for major inference frameworks.
The DFlash approach replaces speculative decoding’s autoregressive drafter with a block diffusion model and injects target‑model hidden features into every KV‑cache layer, achieving up to 5× speed‑up for Qwen3.5‑27B on single‑GPU and 1.5–1.9× on high‑concurrency workloads while preserving output quality.
