Chunked Prefill: Solving the Noisy Neighbor Problem in Inference
When a massive prompt stalls your entire inference server, you have a noisy neighbor problem. The solution requires rethinking how we process context with Chunked Prefill.
Tag: Inference
TTFT vs ITL: The Two Metrics Defining Inference Performance
Average latency is a lie that hides tail-end failures. To truly optimize AI inference in 2026, you must separate your Time To First Token from your Inter-Token Latency.
Speculative Decoding Infrastructure: Squeezing Latency without Hardware Upgrades
The bottleneck for LLMs is memory bandwidth, not compute. Discover how to use speculative decoding on GCP to achieve 3x speedups by using small "draft" models to accelerate massive "oracle" models.
Single-Batch Inference: Speculative Decoding on an A100
See how speculative decoding performs for single-batch requests on an NVIDIA A100. We analyze acceptance rates, latency, and the mechanics of the draft model gamble.
· StrategySqueezing the Inference Lever: The Economics of LLM Throughput
Inference price isn't a fixed cost-it's an engineering variable. We break down the three distinct levers of efficiency: Model Compression, Runtime Optimization, and Deployment Strategy.