Inference Optimization & KV Cache Engineering
High-throughput LLM serving: Speculative decoding, FlashAttention-3, FP8 KV caching, and continuous batching.
Pillar Architectural Overview
### Overcoming the Memory Bandwidth Bottleneck
In autoregressive token generation, the core constraint is rarely FLOPS; it is **High Bandwidth Memory (HBM) bandwidth**. Every generated token requires transferring hundreds of gigabytes of weights and KV states across the memory bus.
$\text{Generation Latency} = \frac{\text{Model Weight Bytes} + \text{KV Cache Bytes}}{\text{HBM Bandwidth (TB/s)}}$
Core optimization pillars:
- **Speculative Decoding**: Using a lightweight draft model to hypothesize $K$ tokens in parallel, verified in one forward pass by the target model.
- **Quantized KV Caches (FP8/INT4)**: Cutting KV memory footprints by 50–75% with zero degradation in needle-in-a-haystack retrieval.
- **Asynchronous Execution & Continuous Batching**: Eliminating GPU idle time by dynamically injecting new requests into active generation batches.