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Sonder Inference

Low-latency Model Execution

Optimized for quantized open-weights models, Sonder Inference provides dynamic KV-cache management and continuous batching for high-concurrency serving.

Core Technical Primitives

Sonder Inference is engineered with foundational primitives for maximum throughput and efficiency in distributed ML environments.

Dynamic KV-Cache

Continuous Batching

Tensor Parallelism

Efficiently manage key-value caches for large language models, reducing memory footprint and improving token generation speed.

Maximize GPU utilization by processing multiple requests in a single batch, eliminating idle time between inferences.

Distribute model computations across multiple GPUs or nodes, enabling deployment of massive models with minimal overhead.

Performance Metrics

Throughput That Scales

1200+

Tokens/second

50ms

Time-to-first-token

99%

GPU utilization

Zero

Memory copy overhead

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Launch Inference Server

Clone the repository and spin up your first Inference server locally in minutes. Native gRPC streaming endpoints are ready.