Open-weight models are fast on Neon AI Gateway. Here's why

Neon AI Gateway serves Databricks-hosted open-weight models through Databricks Foundation Model APIs, using an inference stack designed to improve real-world latency and throughput for workloads such as coding agents and multi-step tool loops. The post attributes performance to continuous batching, paged KV-cache allocation, combined prompt-prefill and token-decoding scheduling, TensorRT-LLM-based runtimes with custom GPU kernels, quantization, and hardware-specific multi-GPU tuning, particularly for Mixture-of-Experts models. It emphasizes prompt caching as especially useful for agents that repeatedly send stable system prompts, tool definitions, and examples, citing a Databricks batch-inference deployment of gpt-oss where a roughly 30% cache-hit rate corresponded with 2.5 times higher per-replica input-token throughput and three times lower median latency. Developers can access the gateway through compatible existing SDKs, pay standard model token rates without a Neon surcharge, and, during the beta period, use free tokens on eligible Neon plans in AWS us-east-2 to evaluate performance on their own workloads.

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