---
title: "SGLang | Aguasto"
description: "Technical reference for SGLang: Serve supported language and multimodal models with prefix caching and multi-GPU inference. Hardware assumptions and operating trade-offs are documented."
canonical: "https://www.aguasto.com/inference-stack/sglang"
last-updated: "2026-10-12"
---

# SGLang | Aguasto

Canonical HTML: [SGLang](https://www.aguasto.com/inference-stack/sglang)

> Technical reference for SGLang: Serve supported language and multimodal models with prefix caching and multi-GPU inference. Hardware assumptions and operating trade-offs are documented.

[Aguasto](https://www.aguasto.com/) [Inference Stack](https://www.aguasto.com/inference-stack)  SGLang

[Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools)

Inference engine

## SGLang

Serve supported language and multimodal models with prefix caching and multi-GPU inference.

Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark.

Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services.

### When it is relevant

- Evaluate a serving engine for repeated-prefix or tool-use workloads

- Study distributed inference with an exact checkpoint and parser

[SGLang official documentation overview (external source)](https://docs.sglang.io/)   · [SGLang hardware platforms (external source)](https://docs.sglang.io/docs/hardware-platforms/overview)

### Hardware and software assumptions

Documented ecosystem: NVIDIA, AMD, Intel, Google, Huawei, Moore Threads. Platform support is not a guarantee that every checkpoint, optimization or feature works on every device.

- Confirm model, tool-parser, precision and hardware support in the pinned release

- Measure the actual prompt mix, concurrency, context and output lengths

- Aguasto has not tested this stack or its performance

[SGLang official documentation overview (external source)](https://docs.sglang.io/)   · [SGLang hardware platforms (external source)](https://docs.sglang.io/docs/hardware-platforms/overview)

### Operating trade-offs

- Prefix-cache gains depend on reusable prompts and scheduling

- A supported hardware platform does not imply every model and optimization works on it

[SGLang official documentation overview (external source)](https://docs.sglang.io/)   · [SGLang hardware platforms (external source)](https://docs.sglang.io/docs/hardware-platforms/overview)

### Continue the research

[Choosing an inference framework](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) [Prefill, decoding and disaggregation](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) [Benchmark methodology](https://www.aguasto.com/research/inference-benchmark-methodology) [Model references](https://www.aguasto.com/models) [Infrastructure collaboration](https://www.aguasto.com/partners)

### Sources & verification

Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production.

- [SGLang official documentation overview ↗](https://docs.sglang.io/)

language and multimodal serving; RadixAttention and prefix caching; multi-GPU inference; documented hardware platforms. Publication: date not stated in the reviewed source. Verified: 2026-10-12.

- [SGLang hardware platforms ↗](https://docs.sglang.io/docs/hardware-platforms/overview)

Separate installation paths by hardware platform. Publication: date not stated in the reviewed source. Verified: 2026-10-12.

### Reference journey

[Browse the stack ↗](https://www.aguasto.com/inference-stack) [Model Index ↗](https://www.aguasto.com/models) [GPU Library ↗](https://www.aguasto.com/gpus) [Sources & verification ↓](https://www.aguasto.com/inference-stack/sglang#sources)

Research reference · No available Aguasto service

## Company navigation

- [Home](https://www.aguasto.com/)
- [Infrastructure](https://www.aguasto.com/infrastructure)
- [GPU Cloud](https://www.aguasto.com/gpu-cloud)
- [AI Inference](https://www.aguasto.com/ai-inference)
- [Development & Investment](https://www.aguasto.com/development)
- [Company](https://www.aguasto.com/company)
- [Contact](https://www.aguasto.com/contact)
- [Privacy](https://www.aguasto.com/privacy)
- [Resources](https://www.aguasto.com/resources)
- [GPU Library](https://www.aguasto.com/gpus)
- [Model Index](https://www.aguasto.com/models)
- [Inference Stack](https://www.aguasto.com/inference-stack)
- [Learn](https://www.aguasto.com/learn)
- [Insights](https://www.aguasto.com/insights)
- [Research](https://www.aguasto.com/research)
- [Industry cases](https://www.aguasto.com/research/cases)
- [Why Modular](https://www.aguasto.com/why-modular)
- [Partners](https://www.aguasto.com/partners)
- [GPU comparison](https://www.aguasto.com/gpus/compare)
- [Model comparison](https://www.aguasto.com/models/compare)
- [Engineering tools](https://www.aguasto.com/tools)
- [Development friction](https://www.aguasto.com/research/hyperscale-development-friction)
- [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100)
- [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200)
- [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200)
- [NVIDIA B300](https://www.aguasto.com/gpus/b300)
- [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72)
- [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72)
- [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72)
- [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x)
- [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x)
- [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x)
- [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x)
- [AMD Helios](https://www.aguasto.com/gpus/helios)
- [GLM-4.5](https://www.aguasto.com/models/glm-4-5)
- [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b)
- [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1)
- [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct)
- [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it)
- [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b)
- [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct)
- [vLLM](https://www.aguasto.com/inference-stack/vllm)
- [SGLang](https://www.aguasto.com/inference-stack/sglang)
- [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm)
- [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo)
- [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton)
- [AMD ROCm](https://www.aguasto.com/inference-stack/rocm)
- [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve)
- [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes)
- [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory)
- [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology)
- [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving)
- [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework)
- [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference)
- [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics)
- [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability)
- [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures)
- [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now)
- [Official LinkedIn](https://www.linkedin.com/company/aguasto)

This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.