Speculative Decoding With Eagle3 And Mtp Strategies

NVIDIA's LLM inference optimizer — quantization, kernel fusion, maximum GPU performance.

Unique: Implements pluggable speculation strategies (EAGLE3, MTP, custom) with batch verification that validates multiple candidate sequences in parallel. Integrates with PyExecutor's scheduling to overlap draft model generation and verifier validation, reducing latency by 30-50% with minimal accuracy loss.

vs others: More flexible than vLLM's speculative decoding (which only supports simple draft models) and more efficient than naive implementations through batch verification. EAGLE3 integration provides 40-50% latency reduction on common models vs 20-30% for simpler draft models.

via “speculative decoding with eagle draft model integration”

Fast LLM/VLM serving — RadixAttention, prefix caching, structured output, automatic parallelism.

Unique: Integrates EAGLE draft model predictions directly into the request scheduling pipeline, batching verification of draft tokens with main model forward passes to minimize overhead. Tracks per-request acceptance rates and adapts draft depth dynamically.

vs others: Achieves 1.5-3x speedup on decode-heavy workloads compared to non-speculative generation, with lower overhead than naive speculative decoding by batching verifications and integrating with the scheduler.