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Layers / Inference / Serving / Inference engines / vllm

vllm

UNCLAIMED

INFERENCE ENGINES

vllm — A high-throughput and memory-efficient inference and serving engine for LLMs. Listed under Inference engines in the Inference / Serving layer. 9 of 9 comparison attributes are recorded, each with the page it was read from.

High-throughput and memory-efficient inference and serving engine for Large Language Models. Deploy AI faster with state-of-the-art performance.

4 OF 4 SIGNALS HELD

WebsiteDocsvllm-project/vllm

What it runs on, and what runs on it

22 cited edges
The differentiator

Computed from the stack graph, both directions. Every cited edge links the page it was read from; a vendor-added one links the vendor’s own source — this is the reason to be here rather than on the vendor’s own site.

RUNS ON / INTEGRATES WITH

USED BY

Open-source signals

LICENCE
Apache-2.0
2026-09-21JSON
DOWNLOADS 30D
not measured
LAST COMMIT
not measured

A HOLLOW RING IS UNMEASURED, NOT ZERO · WE DO NOT SUBSTITUTE A PARENT COMPANY’S REPOSITORY FOR A PRODUCT THAT HAS NONE

Price

We hold no price for this product.

This is open-source software: you can run it yourself and the licence costs nothing. If the project also sells a hosted service, we do not yet hold the page it prices it on. If that changes — or if you publish /.well-known/pricing.json — it appears here with a capture date.

How we read prices →

What its categories ask

9 answered · 0 unanswered

9 questions declared by Inference engines. Most rows across the directory read like these.

ATTRIBUTEANSWEREVIDENCE · SOURCE
Continuous batchingYes
“Advanced scheduling and continuous batching ensure peak GPU utilization.”
2026-09-12
Deploymentboth
“Deployment Deployment Async LLM Streaming Helm Charts LLM Engine Example Sagemaker-Entrypoint Disaggregated Disaggregated Disaggregated Encoder Disaggregated Serving Ec Both Encoder Disaggregated Prefill V1 Flexkv Connector KV Load Failure Recovery Test LMCache Examples Mooncake Connector”
2026-09-13
HardwareNVIDIA CUDA GPU, AMD ROCm GPU, Huawei Ascend NPU, AWS Neuron Accelerator, Google Cloud TPU, IBM Spyre Accelerator, Intel Gaudi XPU, CPU, Apple Apple Silicon, Baidu Kunlun XPU, Cambricon MLU
“Unified API across platforms NVIDIA CUDA GPU Popular AMD ROCm GPU Huawei Ascend NPU AWS Neuron Accelerator Google Cloud TPU IBM Spyre Accelerator Intel Gaudi XPU CPU Apple Apple Silicon Baidu Kunlun XPU Cambricon MLU”
2026-09-12
LicenceApache-2.02026-09-11JSON
Open sourceYes2026-09-11JSON
OpenAI-compatible APIYes
“Includes a drop-in OpenAI-compatible API for instant integration.”
2026-09-12
QuantizationModel quantization
“LLM Compressor · Model quantization”
2026-09-13
StreamingYes
“Streaming OpenAI Responses Client With Mcp Tools OpenAI Responses Client With Tools General General vLLM V1”
2026-09-13
Structured outputYes
“Structured Outputs”
2026-09-13

A ROW WITH NO ANSWER IS NOT A ROW WITH A “NO” · A VENDOR MAY ANSWER A BLANK ONE, MARKED VENDOR CLAIMED WITH ITS SOURCE · A DOCUMENTED VALUE IS DISPUTED, NOT OVERWRITTEN, AND STAYS VISIBLE WHILE WE RE-READ THE SOURCE

Practitioner reviews

Nobody has written one yet.

A review here is structured, not a star: hardware, scale band, what broke in production, and whether you would choose it again.

NO PERCENTAGE IS SHOWN UNDER FIVE REVIEWS · NO STAR RATINGS, EVER

Also in this category

47 products in Inference engines. Comparison defaults to within the category — pairing this with a GPU cloud would produce a page that means nothing.

AnyscaleUSED BY 30COMPARE →
OllamaUSED BY 20COMPARE →
SGLangUSED BY 5COMPARE →
CTranslate2USED BY 3COMPARE →
LMDeployUSED BY 2COMPARE →
BasetenUSED BY 1COMPARE →
atomic-chatNO DEPENDENTS RECORDEDCOMPARE →
audio.cppNO DEPENDENTS RECORDEDCOMPARE →

USED BY = LISTED PRODUCTS DEPENDING ON IT, IN THIS DIRECTORY

Frequently asked

Is vllm open source?
Yes — it is listed as open source, and its repository is the source for the activity figures on this page rather than anything the project says about itself.
What does vllm run on?
Anthropic, OpenAI. Every one of those is an edge we recorded from documentation that states it, not an inference from what the two products have in common.
Does vllm offer continuous batching?
Yes. Read from their own page: “Advanced scheduling and continuous batching ensure peak GPU utilization.”.
Does vllm offer openai-compatible API?
Yes. Read from their own page: “Includes a drop-in OpenAI-compatible API for instant integration.”.
What does a blank mean on the vllm page?
That nobody has told us, not that the answer is no. A hollow ring is "no answer on record", which is a different state from a recorded no — and the two look different on purpose. A blank is an absence of evidence, and this page shows how many of its questions are in that state rather than quietly leaving them out.
Where do the vllm figures come from?
Each one is read from a public page — usually the vendor's own documentation or pricing page — and carries a link to it with the date it was read. Nothing here is estimated, averaged across sources, or inferred from a similar product. If a figure cannot be traced to a page, it is not published.
Can vllm change what this page says?
They can claim the listing and supply or correct values, which are then labelled as theirs. They cannot remove the page, hide a comparison they are losing, or change a figure somebody else recorded with a source. Nothing on the page is for sale. How the site is paid for is set out under money and independence at /our-data#sec5.
Something here is wrong — how do I fix it?
Dispute the value. Every figure links to the page it was read from, so a dispute names the value and gives a source that contradicts it. That is the only lever anyone has over this page, and it is the same lever for the vendor, a competitor and a reader.
Is this listing run by vllm?
No. It is built from public sources, and no value on it came from the company. Anyone with an address at the company’s own domain can claim it for free. A claim lets the company write the tagline, description and logo, and answer blank questions or add dependencies, each marked as theirs with a source. Prices, licence and OSS figures stay as the directory read them, category changes only through our review, and each can be disputed with a source.

Cite this page

A model that quotes us should quote us correctly. The reading and its date sit inside the citation rather than beside it, because a quotation carrying only a URL goes wrong the week the URL changes.

infratoapp, “vllm — Inference engines”, read 12 September 2026. Continuous batching: Yes as published at vllm.ai. https://www.infratoapp.com/products/vllm

What vllm can and cannot change here

Claiming the organisation lets its admins and editors change the tagline, description, logo, docs link and deployment model of every product that org owns — live, labelled as theirs, and logged. Answers to blank questions and dependencies go live too, marked as the owner’s and sourced; a documented answer cannot be overwritten. Everything else on this page is a stated fact with a dispute route: price, licence and OSS figures. Those columns are ungranted at the database level, so they are not a feature we withheld — they are a write that cannot happen. Name, category, website, pricing page and package names can be changed by the owner only through our review. A dispute never deletes a value; it marks it and publishes the correction.

Claim vllmFree · email at the company’s own domain