How it works
An inference layer built for less waiting. Vakone orchestrates multiple tiers of LLMs to reduce response latency.
The system
- 01Multi-tier orchestrationA coordinated stack of models works at different levels. Vakone schedules inference across those tiers and gives the user-facing request priority.
- 02Continuous attentionAttention over the live token stream is computed incrementally. KV-cache state is built continuously as the input evolves.
- 03Deterministic by designVakone and Baseline use the same answer-model weights and settings. Deterministic inference is designed to keep their outputs identical; in our published runs, nearly every answer matched byte for byte.
- 04Measured liveLatency is measured on the server from submission to the first visible answer token and to the complete answer. Every outcome remains visible.
Live token stream
Incremental attention · continuous state · priority scheduling
Fair comparison
- The same answer-model weights and settings
- The same GPU class
- Server-side measurements from one monotonic clock
- When there is nothing to gain, the result is a tie: the user-facing request always gets priority
Both sides use the same answer model and settings. The answer arrives sooner when the orchestration layer reduces the remaining latency. No artificial delay is added to Baseline.
What is measured
- Enter → first answer token
- Server time from the moment the submit is received to the first token of the visible answer. Hidden thinking tokens do not count as the answer.
- Enter → answer complete
- Server time from submit receipt to the last token of the answer.
- Less wait
- How much shorter Vakone's Enter → first answer token was than Baseline's. Within 10% of each other counts as a tie.
- Server clock
- All resting numbers come from one monotonic clock on the server, so they cannot drift with your device's clock.
Privacy
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