Seeing the Plumbing: Profiling vLLM Speculative Decoding on NVIDIA Blackwell
Conference Context
- Date/time: 2026-07-01 · 11:40am-12:00pm
- Track/room: track TBD · Expo Stage 2 NW
- Speaker(s): Sheilah Kirui
- Session type/status: session · confirmed
- Track: track TBD
- Room: Expo Stage 2 NW
- Session type: session
- Status: confirmed
Session Description
Speculative decoding promises dramatic LLM speedups by using a tiny draft model to guess tokens ahead of a large target model. However, dual-model serving fundamentally rewrites your memory dynamics and introduces a rigid engineering trade-off: guess right, and you bypass the memory-bandwidth bottleneck; guess wrong, and you waste compute. This session is a live-demo routing identical workloads through baseline and speculative configurations in vLLM on a single NVIDIA RTX 6000 Blackwell GPU. Splitting the screen between a Streamlit app and a live Grafana dashboard, we will profile the inference engine across three vectors: Time per Output Token (TPOT): The real-time, user-facing latency delta. KV Cache & Memory Footprint: The exact VRAM tax of tracking parallel token states within a 96GB budget. Draft Acceptance Rate: Visualizing the tipping point where dropping acceptance rates cause speculative decoding to fall below baseline efficiency. Supporting Materials Project Repository: https://github.com/akamai-developers/speculative-decoding-example-vllm-blackwell# (Work In Progress / Active Development)
Media Evidence
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Evidence Graph
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Synthesis
Synthesized Breakdown
Seeing the Plumbing: Profiling vLLM Speculative Decoding on NVIDIA Blackwell ## Conference Context - Date/time: 2026-07-01 · 11:40am-12:00pm - Track/room: track TBD · Expo Stage 2 NW - Speaker(s): Sheilah Kirui - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 2 NW - Session type: session - Status: confirmed ## Session Description Speculative decoding promises dramatic LLM speedups by using a tiny draft model to guess tokens ahead of a large target model. However, dual-model serving fundamentally rewrites your memory dynamics and introduces a rigid engineering trade-off: guess right, and you bypass the memory-bandwidth bottleneck; guess wrong, and you waste compute. This session is a live-demo routing identical workloads through baseline and speculative configurations in vLLM on a single NVIDIA RTX 6000 Blackwell GPU. Splitting the screen between a Streamlit app and a live Grafana dashboard, we will profile the inference engine across three vectors: Time per Output Token (TPOT): The real-time, user-facing latency delta.
Speaker And Company Context
- Sheilah Kirui — role not listed at NVIDIA.
Topics Covered
Derived Links And Source Material
Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.
Evidence Boundary
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