Prime Inference: Fast, Reliable Serving for Frontier Open Models

Prime has launched Prime Inference, an OpenAI-compatible serving platform for frontier open-source models that offers serverless endpoints and reserved capacity across multiple datacenters, extending its post-training infrastructure into a continual-learning loop that can feed production experience back into model training. The platform has supported internal reinforcement-learning rollouts, synthetic data generation, evaluations, and coding agents at nearly one trillion tokens daily, while production customer deployments have operated since January; its public GLM-5.3 endpoint on OpenRouter launched September 22 with reported 100% uptime and near-zero tool-call errors. Designed for sustained agent workloads with long contexts, the system separates prefill and decode GPU pools using NVIDIA Dynamo and vLLM, applies KV-aware routing and multi-tier caching with Mooncake, and uses automatic failover, circuit breakers, health monitoring, and overflow capacity to support availability. Prime reports that this architecture reduced p90 inter-token latency by nearly 40%, while optimizations including DEP8 prefill topology, smaller prefill token budgets, NVFP4 KV-cache compression, custom FlashInfer kernels, and block-major KV layouts improved cache capacity, scheduling, and data-transfer efficiency on GB200 NVL72 hardware. The company also added structured tool-call enforcement and parsing fixes to improve agent reliability, contributed components upstream to Dynamo and FlashInfer, and plans to add lower-cost batch and asynchronous inference plus dedicated deployments for reserved capacity and fine-tuned models.

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