Tag archive
A serving setup for Qwen3.8-27B on a single RTX 3090 with vLLM whose README measures when `DFLASH_TOKENS=15` pays and what it costs in reque
Discover how AMD GPUs now run speculative decoding in vLLM, speeding up AI agents and easing production workloads for builders.
LFM2.5-DSpark ships 3.2x on an H100 and 1.18x on a MacBook: the 20 August 2026 numbers...
30B parametri sotto Apache 2.0, distillazione da un modello chiuso, quantizzazione aggressiva e...
Inco AI released DFlash 2, a block-diffusion drafter for speculative decoding that reaches 3.43 times the throughput of ordinary decoding on a single request, and published benchmark tables showing the advantage shrinking to almost nothing under heav
The most widely used local AI inference engine published its first semantic version tag on August 17, 2026, after years of releasing by build number, while a pending contribution adds a speculative-decoding mode that adjusts its own draft depth on th
llama.cpp release b10228 merged speculative-decoding support for DeepSeek V4 Flash, but the new 0731 checkpoint embeds DSpark and ships no MTP at all, so the widely repeated "MTP support landed" advice points users at a head their model does not cont
vLLM speculative decoding in 2026: P-EAGLE vs DFlash vs DSpark for faster, cheaper LLM...
Speculative decoding uses a small draft model to propose K tokens, then verifies all K+1 in one big-model forward pass. Same weight load, mathematically identical output, zero quality loss — typical 2–3× speedup.
A published benchmark shows DeepSeek's 284-billion-parameter V4 Flash generating 32 tokens per second entirely on one AMD Strix Halo machine, using aggressive quantization, speculative decoding and reduced expert routing.
DeepSpec offers a full-stack codebase for speculative decoding, impacting Python developers and AI engineers. Learn how it can reshape your projects.
If you've been running models locally, you already know the drill. Every token an LLM produces...