Baseten has established itself as a leading platform for mission-critical LLM inference, focusing on serving large language models in production with strict requirements around latency, reliability, and compliance. This case study explores their technical architecture and approach to solving enterprise-grade LLM deployment challenges. The company's platform is built around three fundamental pillars that they've identified as crucial for production LLM deployments: ### Model-Level Performance Optimization The first pillar focuses on optimizing individual model performance. Baseten supports multiple frameworks, with particular emphasis on TensorRT-LLM and SGLang. The company has made significant contributions to these frameworks, particularly SGLang, which they've helped optimize for large models like DeepSeek-V3 (671B parameters). Their platform implements various optimization techniques: * Prefix caching with SGLang for improved KV cache utilization * Support for blockwise FP8 quantization * Specialized attention variants like multi-latent attention * KV cache-aware load balancing across model replicas * Support for speculative decoding with draft models ### Horizontal Scaling Infrastructure The second pillar addresses the challenge of scaling beyond single-node deployments. Baseten has built sophisticated infrastructure that goes beyond basic Kubernetes automation: * Cross-region and cross-cloud model deployment capabilities * Ability to handle massive horizontal scaling (50+ replicas in GCP East, 80+ in AWS West, etc.) * Support for customer-owned cloud infrastructure while maintaining centralized management * Sophisticated load balancing that considers KV cache state, queue sizes, and geographical location ### Complex Workflow Enablement The third pillar focuses on supporting sophisticated multi-model workflows. They've developed "Truss Chains" to enable low-latency multi-step inference pipelines. A notable example is their work with Bland AI for AI phone calls, achieving sub-400ms latency through: * Streaming data between multiple models (speech-to-text, LLM, text-to-speech) * Eliminating network round-trips between model calls * Independent scaling of each model while maintaining end-to-end performance ### Technical Implementation Details Baseten's platform is built on several key technologies: * Truss: Their open-source model packaging and deployment library * Custom Triton Inference Server implementation for enhanced reliability * Support for multiple frameworks including SGLang, TensorRT-LLM, and vLLM * Implementation of state-of-the-art optimizations like structured output generation using X-Grammar The platform is designed to handle various deployment scenarios: * Dedicated inference resources for each customer * Support for customer-owned cloud infrastructure * Multi-cloud deployments for redundancy and compliance * Region-specific deployment for latency optimization ### Real-World Impact and Use Cases The platform has been particularly successful in serving: * Foundation model companies requiring sophisticated pre-trained model deployment * Healthcare companies needing HIPAA-compliant inference with medical jargon understanding * Companies requiring sub-second latency for complex multi-model workflows * Enterprises with strict compliance and geographical requirements ### Challenges and Solutions Some key challenges they've addressed include: * Managing extremely large models like DeepSeek-V3 requiring specialized hardware (H200 GPUs) * Implementing sophisticated load balancing considering KV cache state * Building reliable multi-region deployment capabilities * Ensuring consistent performance across different frameworks and hardware configurations ### Future Directions Baseten continues to evolve their platform with focuses on: * Enhanced support for emerging frameworks like SGLang * Improved developer experience for complex workflow creation * Advanced optimization techniques for large model inference * Cross-cloud and cross-region deployment capabilities The case study demonstrates how production LLM deployment requires much more than just model serving - it needs a comprehensive platform that can handle complex workflows, ensure reliability, maintain performance at scale, and meet enterprise requirements around compliance and geographical distribution. Baseten's approach of building around three core pillars (model performance, horizontal scaling, and workflow enablement) provides a blueprint for understanding the requirements of production LLM systems.