Agentic workloads (many concurrent, tool-enabled LLM requests with long KV lifetimes and frequent context switches) expose a different bottleneck than simple single-request throughput. TokenSpeed targets that regime: it trades conventional one-size-fits-all inference design for an architecture that encodes parallelism, resource ownership, and request lifecycle in the runtime so agent workloads can reach very high sustained TPS on modern GPUs.
What Sets It Apart
- Local‑SPMD modeling + static compiler: instead of hand-written parallelism logic, module‑boundary placement annotations drive a static compiler that emits collective communication. This reduces manual sharding complexity and yields consistent multi‑GPU behavior across models.
- Scheduler with type‑safe KV ownership: a C++ control plane and Python execution plane implement request lifecycle as a finite‑state machine; the type system enforces safe reuse of KV cache regions to enable aggressive resource overlap without correctness bugs.
- Pluggable kernel stack + MLA on Blackwell: layered kernel API and registry allow optimized kernels for different hardware; TokenSpeed ships one of the fastest Multi‑head Latent Attention (MLA) implementations optimized for Blackwell-class GPUs, enabling its high agentic throughput benchmarks.
- Low‑overhead entrypoint: SMG‑integrated AsyncLLM for minimal CPU-side handling costs, designed to keep GPU stalls low when serving asynchronous agentic tasks.
Who It's For + Tradeoffs
Great fit if you operate production LLM services that run agentic workloads (tool use, long-horizon multi‑step agents) and need highest sustained TPS on GPU clusters. TokenSpeed is tuned for frontier model enablement (day‑0 support reported for several recent models) and demonstrates record numbers in agentic benchmarks (examples show ~580 TPS on a large Qwen3.5 configuration).
Look elsewhere if you need a tiny single‑GPU toy runtime, prefer extremely simple deploys without custom kernel requirements, or must run exclusively on CPU‑only infrastructure; TokenSpeed’s gains come from invested engineering in GPU kernels, scheduler semantics, and cluster deployment practices.
Where It Fits
Positioned between TensorRT‑LLM (throughput focus) and vLLM (usability for many small requests), TokenSpeed aims to combine TensorRT‑level performance with vLLM‑style ergonomics for agentic production workloads. Use it when your workload needs both high per‑GPU throughput and robust asynchronous request handling at scale.
