Choose a model without accepting a black box

InferCrane’s Verified Models catalog is a small, reviewed set of deployment starting points. It is not a hosted-model marketplace and it does not imply that one model runs on every GPU, runtime, or provider.

infercrane models
infercrane models embeddings
infercrane models inspect mistral-7b-instruct

Each entry exposes:

the Hugging Face repository and immutable commit;
publisher, task, protocol, capabilities, and modalities;
license URL and gated-access requirement;
reviewed runtime configuration and serving profiles;
review date and an explicit evidence boundary.

Create a project from one reviewed entry:

infercrane workload init ./support-model \
  --recipe mistral-7b-instruct

cd ./support-model
infercrane workload validate
infercrane workload plan
infercrane workload deploy --wait

The browser console exposes the same control-plane catalog under Models. A model detail page shows the immutable identity and qualification scope before the deployment form is populated.

Evidence levels

Level	What it proves	What it does not prove
Configuration verified	Repository identity, immutable revision, license metadata, protocol, and reviewed runtime configuration	GPU fit, provider capacity, latency, throughput, or price
Measured recipe	One exact artifact, runtime, provider, GPU, region, workload, and AIPerf run	Portability to a different serving plan
Community recipe	Contributor-supplied configuration with visible provenance	InferCrane qualification unless separately recorded
Explicit model	The operator selected a repository and serving plan	Compatibility or production readiness

The current built-in catalog contains only configuration-verified entries. Measured results stay tenant-scoped in benchmark history and immutable model recipes; InferCrane does not silently promote them into universal claims.

Current collections

The catalog follows application tasks rather than a single popularity ranking. Selection considers current ecosystem adoption, an official upstream model card, license clarity, and compatibility with InferCrane’s qualified runtime line.

Collection	Reviewed starting points	Typical application
General chat	Mistral 7B Instruct, Qwen3 8B, Llama 3.1 8B Instruct	Support, assistants, generation
Reasoning	Qwen3 8B, DeepSeek R1 Distill Qwen 7B	Analysis and multi-step tasks
Coding	Qwen2.5 Coder 7B Instruct	Completion, explanation, transformation, repair
Vision and documents	Gemma 3 4B IT, Qwen2.5 VL 7B Instruct	Screenshots, forms, diagrams, visual question answering
Embeddings and RAG	BGE-M3	Multilingual search and private retrieval
Extraction and summarization	Granite 3.3 8B Instruct	Controlled enterprise text pipelines

This is not a claim that every entry is best for its category. Evaluate quality on the application’s own data, then benchmark the exact serving plan before promotion. Catalog review uses the upstream Hugging Face model metadata, the vLLM 0.8.5 support matrix, and each publisher’s model card and license. Task-oriented discovery is informed by model-library patterns used by Replicate and Baseten, but InferCrane entries remain provider-neutral and do not copy their performance or availability claims.

Popular models that require a newer, unqualified runtime are not presented as one-click templates. They remain available through the explicit-model path, and can enter the reviewed catalog after the runtime and protocol matrix is qualified.

The gpu_hint in a serving profile is an editable configuration starting point. Run plan, confirm model memory/topology requirements, and qualify the exact provider/runtime combination before production.

Any Hugging Face model remains available

The catalog is convenience, not an allowlist:

infercrane workload init ./my-model --model organization/repository
infercrane workload plan ./my-model

Mutable references are resolved into ModelArtifact identity during the durable deployment lifecycle. A catalog entry simply starts with a reviewed commit and clearer license/protocol metadata.

API

Authenticated consoles and automation can use:

curl -sS "$INFERCRANE_CONTROL_URL/api/v1/catalog/models?query=chat" \
  -H "Authorization: Bearer $INFERCRANE_API_KEY"

curl -sS "$INFERCRANE_CONTROL_URL/api/v1/catalog/models/qwen3-8b" \
  -H "Authorization: Bearer $INFERCRANE_API_KEY"

The response includes performance_claims: false. Trustworthy performance comes from AIPerf benchmarking, Inference Lab, and the exact evidence attached to a revision.

Start here

Deploy and connect

Operate

Evidence and decisions

Verified Models

Choose a model without accepting a black box

Evidence levels

Current collections

Any Hugging Face model remains available

API

​Choose a model without accepting a black box

​Evidence levels

​Current collections

​Any Hugging Face model remains available

​API

Choose a model without accepting a black box

Evidence levels

Current collections

Any Hugging Face model remains available

API