Choose a supported path before a headline
A serving engine is one layer of an inference service. Begin with the exact model revision, modality, precision and accelerator, then check the project's current supported configuration. A feature listed on a project homepage does not establish that every model and backend combination implements it equally.
Aguasto's proposed selection worksheet separates six questions: checkpoint loading, task-quality preservation, hardware support, scheduling behavior, deployment controls and reproducibility. Choose one representative workload and one constrained hardware category for an initial comparison. Avoid mixing a standalone GPU run with a rack-scale run or a default engine configuration with a heavily tuned competitor.
vLLM: a broad serving starting point
The official vLLM documentation describes a serving library with PagedAttention, continuous batching, prefix caching, streaming and several distributed parallelism strategies. These features make it a relevant candidate when evaluating an open-weight model-serving path. Their presence is documented project capability, not an Aguasto performance result.
For a vLLM evaluation, verify the exact model architecture and quantized checkpoint support, then save engine and backend versions. Check batching and cache settings against the intended prompt-length distribution. Use documented defaults as a reproducible baseline before tuning, and record every changed option so another engineer can explain what produced the result.
vLLM official documentation (external source)SGLang: inspect reuse and workload shape
SGLang's documentation presents a language and multimodal serving framework with RadixAttention, prefix caching and multi-GPU parallelism. It also describes multiple hardware backends. A documented hardware family should be treated as a starting point for a specific compatibility check.
A useful SGLang experiment should distinguish traffic with shared prefixes from independent prompts. Fix the model, tokenizer and sampling settings, then observe both steady-state and burst behavior. Evaluate structured-output correctness if the application needs it. Do not assume that a reuse-oriented mechanism wins on a workload dominated by unrelated long generations; measure the shape that the buyer actually intends to run.
Welcome to SGLang — official documentation (external source)TensorRT-LLM: the NVIDIA-specific path
NVIDIA describes TensorRT-LLM as an open-source inference library for NVIDIA GPUs. The observed documentation presents a PyTorch-native architecture, a high-level LLM API and single- or multi-device deployment paths, with hardware support listed separately. It would be outdated to explain the current project exclusively as a mandatory ahead-of-time engine-building process.
For a TensorRT-LLM candidate, review its supported model and precision path on the chosen NVIDIA architecture. Pin the release and container, and follow that release's deployment instructions. Manufacturer performance statements remain attributed claims until reproduced under the selected workload. A vendor-specific implementation can be useful without implying universal superiority.
Overview — NVIDIA TensorRT LLM (external source) · Supported Hardware — NVIDIA TensorRT LLM (external source)Keep the runtime and control plane distinct
AMD's ROCm vLLM guide shows why engine choice cannot be detached from runtime, platform and dependency compatibility. It provides version-specific environments and known issues. For an AMD evaluation, use a documented supported combination rather than assuming that an NVIDIA-oriented command or quantization kernel transfers unchanged.
Separately decide how replicas are deployed, monitored and upgraded. A model engine, a multi-model server, an inference coordinator and a cluster scheduler are different responsibilities. Profile Ray Serve, Triton, Dynamo and Kubernetes according to their roles rather than putting every tool in one engine-ranking table. Record which layer owns routing, admission, health and recovery.
vLLM inference and serving on ROCm — AMD (external source)Select on evidence the team can maintain
The proposed decision record should show task quality, latency distribution, qualified throughput, peak runtime memory, failures, configuration complexity and observed cost for the same test window. Include the time needed to reproduce a run and roll back a release. A small reliable deployment path can be preferable to a promising configuration that cannot be maintained.
There is no Aguasto winner in this comparison: no comparative hardware run has been executed. Use the framework profiles to shortlist supported paths and the benchmark methodology to test them. Publish a measured conclusion only with environment artifacts, workload definitions and raw observations that explain where the recommendation applies.
Sources & verification
Reference revision 1 · Reviewed . Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production.
- vLLM official documentation ↗
Continuous batching, prefix cache, PagedAttention and distributed serving features; Version-specific support needs verification. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- Welcome to SGLang — official documentation ↗
RadixAttention, prefix caching and multi-GPU parallelism; Project-documented hardware/ecosystem support. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- Overview — NVIDIA TensorRT LLM ↗
NVIDIA-GPU-oriented open-source inference library; PyTorch-native architecture, online/offline APIs and parallelism; Do not describe current software only as a mandatory engine-build workflow. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- Supported Hardware — NVIDIA TensorRT LLM ↗
Named NVIDIA architectures and accelerator support. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- vLLM inference and serving on ROCm — AMD ↗
AMD-specific container/runtime/platform dependencies; Versioned compatibility and known issues. Publication: date not stated in the reviewed source. Verified: 2026-10-12.