Tag: Tokenizer

3 articles

• LLM Tokenizer Preprocessing Pool in Production: Avoiding GPU Starvation with Async Tokenizer Pools, Length-Aware Queues, and CPU Affinity

  The bottleneck in LLM serving isn't always the GPU. This article explains how to decouple the tokenizer preprocessing pool using async batch encoding, length-aware queues, CPU affinity, and backpressure metrics to prevent long prompts from blocking requests and keep the GPU fed with executable batches.

  07/22/2026 LLM Serving / Tokenizer / Performance Engineering / CPU Optimization / vLLM

• LLM Tokenizer Version Governance in Production: Preventing Cost Surprises with Compatibility Fingerprints, Token Drift Replay, and Budget Gates

  Tokenizer changes can silently shift token counts, truncation boundaries, and costs for the same prompt. This article provides practical solutions including compatibility fingerprints, golden corpus replay, dual counting, and budget gates to help teams identify risks before model upgrades and safely roll out changes.

  07/11/2026 LLM Engineering / Tokenizer / Model Migration / Cost Governance

• LLM Chat Template Production Governance: Avoid Model Migration Disasters with Template Versioning, Token Counting, and Replay Testing

  A deep dive into production-grade governance for LLM chat templates, covering template version management, special token handling, precise token counting, tool call rendering, and replay testing to prevent quality regressions and cost overruns during model switches.

  07/07/2026 LLM Serving / Chat Template / Tokenizer / Model Migration