AGUASTO

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.

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) · vLLM GPU installation and compatibility (external source)

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) · vLLM GPU installation and compatibility (external source)

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) · vLLM GPU installation and compatibility (external source)

Continue the research

Choosing an inference frameworkPrefill, decoding and disaggregationBenchmark methodologyModel referencesInfrastructure collaboration

Sources & verification

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

  1. vLLM official documentation ↗

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

  2. vLLM GPU installation and compatibility ↗

    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.