Serverless Inference: Conquering the 5-Second Cold Start
The infrastructure hacks required to make scale-to-zero LLM inference viable for production latency.
Tag: Inference
LiteRT-LM Deep Dive: Engineering LLM Inference for the Edge
How Google's LiteRT-LM framework handles session cloning and KV-cache management to run models like Gemini Nano natively on-device without exploding your memory.
Semantic Caching at Scale: Vector Embeddings for 5x Latency Reduction
Moving beyond exact-match caching for repetitive zero-shot inference workloads. Learn how to architect semantic caching to slash latency and compute costs.
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.
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.