Production-ready inference hosting for open AI models
Deploy newly released open models as fast, reliable APIs by adapting them to a serving stack, optimizing their GPU execution, preserving model fidelity, and operating them against real-world traffic. Customers can begin with shared per-token APIs and move to dedicated deployments when their use case becomes high-volume, reliability-sensitive, or requires custom optimization.
online BOTH AI Agents & Automation
From Latent Space — The Inference Frontier: 10x Faster Models to Self-Optimizing AI — Philip Kiely & Ali Taha, Baseten at 12:30
Problem: Making an open model usable in production rather than merely generating a token from its weights. The service addresses model integration, quantization, speculative decoding, KV-cache routing, GPU parallelism, reliability failures, and performance optimization.
For: Companies experimenting with open models that later develop high-volume, sticky workloads, require predictable reliability, need custom tool calling or structured outputs, or want a dedicated latency-versus-throughput configuration.
Products from this video
Baseten CUDA (Compute Unified Device Architecture) GLM-5.2 GQA NVIDIA Dynamo PyTorch SGLang vLLM
Examples
- GLM-5.2: its production support required quantization, speculative-decoder training, infrastructure setup, testing, and runtime support for its DSA component.
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