Brewcore Labs recommends SGLang - a high-throughput and memory-efficient serving engine for large language models, enabling fast inference with production-ready scalability.
The gold standard for serving large language models
These are just a few of the things your organization can do:
Each solution combines multiple tools from our recommendations to deliver production-ready capabilities.
The best way to serve large models at scale
SGLang delivers superior performance on multi-turn and prefix-heavy workloads.
Its RadixAttention architecture automatically reuses KV cache across requests, achieving significant performance gains for chatbots, RAG pipelines, and multi-turn conversations where prefixes are shared.
By running models locally with SGLang, you can dramatically reduce your AI infrastructure costs compared to using cloud APIs exclusively.
Better GPU utilization means you can serve the same workload with fewer GPUs, reducing hardware requirements.
Keep your data on your own infrastructure with complete control over model inputs and outputs.
No data leaves your environment, ensuring compliance with data protection regulations and internal security policies.
Choose exactly which models to deploy, when to update them, and how to configure them for your specific use cases.
Fine-tune models on your data and deploy them with the same performance optimizations as pre-trained models.
Our team can help you implement SGLang to achieve enterprise-grade performance with large language models.