OneTriangle

Cut large language model inference costs by transferring KV cache across models

Pricing model

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use OneTriangle to run large language model inference at lower latency and reduced computational cost. The service speeds up processing by running the initial prefill phase on a lightweight model and transferring its KV cache directly to a larger model for token generation. By projecting key-value tensors across model architectures, it avoids expensive prefill calculations on large models while maintaining output quality. OneTriangle provides fully managed serving for popular open-weight models, handling infrastructure and GPU allocations through a simple configuration update.

What OneTriangle does

Transfer KV cache tensors between paired model architecturesDeploy managed open-weight models on cloud GPUsConfigure custom endpoints with a single line of configMonitor latency and time to first token across inference runsExecute prompt prefill on lightweight modelsDecode response tokens on high-capacity modelsRoute inference requests through quality-gated fallback pathsCross-model key-value cache transfer between small and large modelsPrefill execution on smaller models with decoding on larger modelsAutomatic fallback to standard prefill if transfer quality thresholds failFaster time to first token on long context promptsManaged GPU infrastructure and dedicated model endpointsDrop-in integration requiring a single line configuration changeSupport for leading open-weight LLMs including Llama, Qwen, and DeepSeek