🏷️ Inference & Systems
🕒 2026-10-06
✍️ Alex Lin · Founder & Principal Systems Architect
📖 ~ 2840 words · 14 min read
LLM Inference Acceleration: PagedAttention, KV Cache Quantization & Speculative Decoding in vLLM
✦ Executive Research Brief · Hardware-Verified in Radar Lab ✦
A comprehensive deep dive into LLM inference bottlenecks. We examine memory-bandwidth bounds, implement FP8/INT4 KV Cache compression in vLLM, and evaluate Speculative Decoding speedups with draft models in high-concurrency environments.
In production deployments of Large Language Models (LLMs), inference cost and throughput directly dictate commercial viability. During autoregressive token generation, every new token requires reloading the entire historical Key and Value tensors from high-latency GPU High-Bandwidth Memory (HBM) to on-chip SRAM. Consequently, token generation is strictly memory-bandwidth bound. In this architecture guide, we dissect PagedAttention, KV Cache quantization, and Speculative Decoding in production vLLM clusters.
01 The Memory Wall: Why KV Cache Consumes 70% of VRAM
For an 80-layer model with Grouped-Query Attention (GQA), a 32,000-token prompt consumes over 10.7 GB of VRAM per request under FP16 precision. When serving dozens of concurrent developers, KV Cache memory consumption quickly causes Out-Of-Memory (OOM) eviction even when tensor cores are idle.
| KV Cache Format |
VRAM per Stream (32k) |
Max Concurrency (Dual 4090) |
Perplexity Degradation |
| Baseline FP16 |
10.74 GB |
4 streams |
Baseline (0.00%) |
| FP8 (E4M3) |
5.37 GB (-50%) |
10 streams |
+0.04% (Imperceptible) |
| INT4 (Group 128) |
2.68 GB (-75%) |
22 streams |
+0.32% (Minor shift) |
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Author: Alex Lin (Founder & Principal Systems Architect)
Former LLM inference infrastructure engineer specializing in distributed vLLM/SGLang optimizations and autonomous terminal agent orchestration.