SGLang Hosting: GPU server comparison

SGLang GPU server for high-performance LLM serving

SGLang is a serving framework for language models and multimodal models. It is designed for low latency, high throughput and efficient reuse of precomputed prompt components. The appropriate GPU server hardware provides the foundation for stable production operation.

Which hardware is suitable for SGLang?

GPU and VRAM must match the model size, quantisation, context length and number of concurrent requests. For large models or high load, multiple GPUs or multiple servers may be required. Network, NVMe storage and system memory also affect model startup, data transfer and scaling.

Typical use cases

• production LLM and multimodal APIs
• chatbots and AI assistants with high throughput
• long or recurring prompt structures
• multi-GPU and distributed inference
• serving Llama, Qwen, DeepSeek and other models

SGLang or vLLM?

Both frameworks are aimed at high-performance model serving. Which solution fits better depends on model support, workload, caching, parallelisation and the operating environment. Therefore also compare the appropriate vLLM GPU servers and the general LLM GPU server comparison.