LLM Reference
CoreWeave

CoreWeave

Researched 4d ago

CoreWeave Inc

Inference

CoreWeave does not have tracked models in LLMReference yet — open the provider docs link above or browse the models index for adjacent hosts.

Covers 0 workload areas across 0 tracked models; last verified 2026-06-29.

Use it for

  • Getting oriented before committing to a specific model

Do not use it for

  • Final benchmark picks without opening the relevant model detail page

Tracked models

0

Models available through this provider

Priced output routes

0

Output pricing not yet tracked

Cheapest output

Unknown

Output pricing not yet tracked

Batch-ready models

0

No batch pricing tracked

Latest model release

Unknown

Release date of the newest tracked model

Freshness

2026-06-29

Researched 4d ago

fresh

Information

Models0
CompanyCoreWeave Inc
Founded2017
Livingston, New Jersey, USA

CoreWeave is a cloud infrastructure company headquartered in Livingston, New Jersey. The company provides a specialized GPU cloud platform with the tagline "The Essential Cloud for AI™", offering GPU instances, AI storage, and infrastructure optimized for deploying AI workloads at scale.

Catalog freshness

No confirmed release dates yet for the models tracked on this provider.

Where this host wins

Not enough capability or benchmark coverage yet to call strengths for this provider.

Getting started

Official product, docs, and pricing links — confirm quotas and regions in the vendor docs.

Compliance notes

No verified compliance claims (SOC 2, ISO, HIPAA) tracked for this provider yet — check the vendor's trust center for current certifications.

Platform Overview

CoreWeave provides GPU cloud infrastructure for AI workloads, including inference-heavy applications. The platform supports GPU instances from NVIDIA and AMD across global data centers with hourly billing, enabling users to deploy and run their own LLM inference workloads using frameworks like vLLM. Does not host pre-trained LLM models.

Compare per-model pricing, input and output token costs, batch availability, and benchmark coverage.

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