12 posts
Sizing the DSpark draft-verification budget from per-request confidence instead of verifying every drafted token, so one configuration holds the throughput/latency frontier from batch size 1 to 256.
How we took GLM-5.2-NVFP4 from 40 ms to 17 ms mean TPOT on 24 B300 GPUs with vLLM: P/D disaggregation, MTP speculative decoding, Model Runner V2, and the SLA-first trade-offs behind the final configuration.
vLLM brings day-0 support to TML Inkling, a 1T-parameter multimodal model, with MTP, long-context serving, parallelism, and up to 380 tokens per second per user on NVIDIA GB200 GPUs.
How Laguna XS.2 is served and optimized in vLLM using first-class model integration, a DFlash speculator trained with Speculators, and FP8, NVFP4, INT4, and INT8 checkpoints from LLM Compressor.
What Speculators v0.5.0 adds for vLLM speculative decoding: DFlash block-diffusion draft models, unified online and offline training, native hidden-state extraction, and Gemma 4 latency results.
How EAGLE 3.1 improves speculative decoding robustness in vLLM with FC normalization, post-norm hidden-state feedback, TorchSpec training support, and config-driven compatibility with EAGLE 3 checkpoints.
How vLLM achieved leading Artificial Analysis results for DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B using open-source kernel fusion, speculative decoding, Blackwell optimizations, and model-specific serving work.
How vLLM extracts verifier hidden states through dummy draft models and KV Connector APIs for speculative decoding, enabling offline and online Speculators training without patching vLLM internals.
How P-EAGLE brings parallel speculative decoding to vLLM by generating multiple draft tokens in one forward pass, with pre-trained drafter heads, config support, and B200 speedups over EAGLE-3.
How Speculators v0.3.0 supports end-to-end Eagle3 draft model training for vLLM, including hidden-state data generation, MoE and non-MoE verifiers, offline workflows, and seamless speculative decoding serving.
How vLLM's inference engine works, covering PagedAttention, continuous batching, prefix caching, speculative decoding, multi-GPU serving, scheduling, and benchmarking for high-throughput LLM workloads.
How speculative decoding works in vLLM, covering EAGLE, Medusa, n-gram proposals, draft and target runners, scheduler and memory-manager changes, and continuous batching for lower token latency.
