Tokens straight off the GPU to the client you already use — and the GPU stops the moment they stop mattering.
Point your OpenAI-compatible client at the line and go. Nothing in your app has to change.
A hang-up, a missed deadline or a newer turn reaches the model and stops the generation. You stop paying for tokens nobody will hear.
ClutchKV saves the attention state of a conversation and loads it back on the next turn. In our vLLM benchmark the follow-up turn took 129 ms instead of 353 ms, with identical output.
The router reads cache and load metrics from Triton and TensorRT-LLM and sends each request to the GPU that suits it.
Inference shares the same transport as voice, video and robotics. One line to operate, not five.
Anything llama.cpp loads — Qwen 2.5, Llama 3 family, Mistral, Gemma, Phi, the lot — plus first-class ONNX Runtime for the embedding/classifier side. GGUF in, OpenAI-compatible SSE out.
For the same reason voice does: a single dropped packet stalls every concurrent stream on HTTP/2 (TCP head-of-line blocking). With 16 concurrent users on one connection, that means everyone's tokens pause on one loss. QUIC streams are independent — only the affected user feels it.
GGUF weights are mmap'd at process start; first token after warm pool is ~30 ms TTFT on a single-shard CPU box. Cold model load (changing model mid-flight) is whatever your disk can do — typically 200–800 ms for a 7B Q4.
Yes. Drop a GGUF into the tenant's weights bucket and point the agent at it. We don't re-quantize; what you upload is what runs.
Both. The default surface is OpenAI-compatible SSE — drop our endpoint into any client that already speaks `openai.chat.completions.stream(...)` and it works. Tool-calling, JSON mode, and streaming function call deltas all pass through. See OpenAI's streaming docs for the wire format we mirror.
ClutchCall ships every modality on the same transport.