The article explains continuous batching, a scheduling technique that re‑evaluates the batch at every inference iteration, eliminating the straggler problem of static batching, and shows how it, together with PagedAttention, can raise LLM service throughput dramatically while balancing latency and memory usage.
Scaling a service from millions to tens of millions of queries per second fails not because of data size but due to per‑request fixed costs, and the article shows how batching aggregates these costs, dramatically boosts throughput, reduces latency, and introduces new challenges such as memory pressure and partial failures.
DeepSeek releases DSpark, an open‑source speculative decoding system that combines semi‑autoregressive generation with confidence‑scheduled verification, delivering 60‑85% per‑user speed gains, lower latency, and superior acceptance rates compared with Eagle3 and DFlash across multiple LLM benchmarks.
The article analyzes how replacing Kafka with a simple in‑memory Go queue reduced architectural complexity, boosted throughput from 240‑330 K to 1.8‑2.0 M messages per second, and clarified debugging, while still acknowledging scenarios where Kafka remains the better choice.
The article introduces NanoFlow, a novel service framework that leverages intra‑device parallelism, operation‑based pipelining, and async scheduling to significantly improve large language model serving throughput, achieving up to 1.91× higher performance while integrating with Alibaba Cloud PAI.
