---
title: "vLLM | Aguasto"
description: "Technical reference for vLLM: Serve supported language and multimodal models using continuous batching, attention-cache management and distributed inference. Hardware assumptions and operating trade-offs are documented."
canonical: "https://www.aguasto.com/inference-stack/vllm"
last-updated: "2026-10-12"
---

# vLLM | Aguasto

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

> Technical reference for vLLM: Serve supported language and multimodal models using continuous batching, attention-cache management and distributed inference. Hardware assumptions and operating trade-offs are documented.

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

[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

## vLLM

Serve supported language and multimodal models using continuous batching, attention-cache management and distributed 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 an OpenAI-compatible model server

- Compare batching, cache use and latency under a defined request workload

[vLLM official documentation (external source)](https://docs.vllm.ai/en/latest/)   · [vLLM GPU installation and compatibility (external source)](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/)

### Hardware and software assumptions

Documented ecosystem: NVIDIA, AMD, Intel, Apple (community Metal plugin). Platform support is not a guarantee that every checkpoint, optimization or feature works on every device.

- Pin a compatible model, engine, PyTorch, CUDA or ROCm build and driver combination

- Check per-model, precision and hardware feature support

- This is a reference stack; Aguasto has not deployed or benchmarked it

[vLLM official documentation (external source)](https://docs.vllm.ai/en/latest/)   · [vLLM GPU installation and compatibility (external source)](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/)

### Operating trade-offs

- Vendor-specific binaries and kernels constrain version combinations

- Available features differ by hardware and backend; documentation support is not a throughput guarantee

[vLLM official documentation (external source)](https://docs.vllm.ai/en/latest/)   · [vLLM GPU installation and compatibility (external source)](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/)

### 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.

- [vLLM official documentation ↗](https://docs.vllm.ai/en/latest/)

continuous batching, PagedAttention and distributed inference; OpenAI-compatible serving; hardware-specific implementations. Publication: date not stated in the reviewed source. Verified: 2026-10-12.

- [vLLM GPU installation and compatibility ↗](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/)

CUDA / ROCm / Intel XPU choices; Apple Metal uses a separate community plugin; PyTorch and runtime binary compatibility constraints; hardware-specific feature matrix. 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/vllm#sources)

Research reference · No available Aguasto service

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- [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.