TokenSpeed's Three-Language Stack: C++ for Correctness, Rust for Agents, Python for Glue
zhyncs42 · x · 2026-10-26
TokenSpeed's author explains why the inference stack uses different languages per layer. Scheduler: C++ — for correctness, not performance; the KV cache state transitions (with PD and L2 cache compatibility) need lossless rollback, and RAII makes state management explicit. Engine: Rust — recently rewritten, with major TTFT gains over Python; crucially, Rust's features pair well with LLM coding agents, keeping iteration speed without sacrificing production safety. Modeling: Python as a flexible glue layer. Takeaway: layer constraints dictate language choice.
Related event: TokenSpeed Open-Source Inference Engine Explains Its Polyglot Design(2 posts)→
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