Runtime

Serving, tuned end to end.

Renting a GPU is the easy part. Token Factory tunes the entire serving path — kernels, batching, caches, and backend selection — per model, so performance comes from the runtime, not just the silicon.

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Forward pass

The forward pass, stage by stage

Every request crosses the same four stages. Each one is tuned, measured, and tuned again — per model, not per fleet.

Kernels & quantization

Fused kernels collapse chains of small operations into single GPU passes, and quantization formats are chosen per architecture — model-specific profiles, not a one-size runtime flag.

Continuous batching & scheduling

New requests join batches already in flight — no batch waits to drain, so short and long generations share capacity without queuing behind each other.

Prefix & KV-cache reuse

Shared system prompts and repeated prefixes are served from cache across concurrent requests instead of recomputed — agent loops and long conversations stay cheap and fast.

Multi-backend routing

Latency-sensitive chat, long-context work, and high-throughput batch each favor a different engine — vLLM, SGLang, or TensorRT-LLM — so the backend is chosen per model and workload shape, not per fleet.

Per-model tuning

Tuned per model, not per fleet

A serving stack that treats every model the same leaves performance behind. Each model in the catalog gets its own runtime profile — quantization choices, batching behavior, cache strategy, and backend fit — validated before it serves a single production token.

Profile

We benchmark the model's architecture against candidate kernels, quantization levels, and backends.

Tune

The serving configuration is tuned for the model's real traffic shape — latency-sensitive chat, long-context work, or high-throughput batch.

Hold

Profiles are re-validated as engines and models update, so performance holds instead of drifting.

Interface

One interface, no stack to run

OpenAI-compatible, fully managed.

Point your existing client at a Token Factory endpoint and serve. No engines to build, no GPUs to schedule, no runtime flags to babysit.

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Built for teams that want:

OpenAI-compatible API surface
Managed endpoints — nothing to operate
Serverless or dedicated capacity, same interface
Tuned runtime included, not a tier

Early Access

See what tuned serving feels like.

Start with serverless access to optimized open models — the runtime work is already done.

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