# Start from an AI platform somebody already checked UNOFFICIAL/EXPERIMENTAL. Teams assembling GPU platforms copy YAML from blog posts, and an H100 misconfiguration has a dollar sign attached. This page lists seven AI platform entries you can read before you run anything, and says what each one needs. AICR is NVIDIA's Apache-2.0 tool for building AI-cluster platforms ([github.com/NVIDIA/aicr](https://github.com/NVIDIA/aicr)). You describe the platform you want and AICR picks the components, orders the installs, and writes the files. It does not decide how your team reviews, approves, or promotes them. That is the part this catalog adds, the same way it adds it to Helm charts. If your immediate goal is to run a model rather than inspect a platform, start with the separate [vLLM CPU example](../../../examples/inference/vllm-cpu-starter/README.md). It runs one real request without a GPU or cloud account. It is not an AICR entry; it is the accessible runtime check beside this platform catalog. If you already have GPU nodes and want to compare their current state, use the [snapshot and diff walkthrough](./snapshot-diff.md). It needs read access to the nodes but no AICR recipe or platform deployment. It shows why a difference is judged against the intended node variant before anyone calls it a fault. ## Which entry should I open? | Entry | What it builds | What you need | Retained version | | --- | --- | --- | --- | | [CPU starter](./cpu-starter.md) | The platform spine without accelerators. Start here to see the mechanics. | Nothing. No GPU, no cloud account, no NGC key. | Derived from AICR v0.14.0 | | [EKS + H100 + Kubeflow](./eks-h100-training-kubeflow.md) | A training platform: EKS, H100 nodes, Kubeflow, a training job. | AWS and GPU capacity to run it. Reading it costs nothing. | AICR v0.14.0 | | [The same platform, four minor versions later](./eks-h100-training-kubeflow-v0-18-0.md) | The same training platform regenerated, so you can see what upstream changed. | As above. | AICR v0.18.0 | | [AICR v0.19.0 with source, objects, variants, and release OCI](./eks-h100-training-kubeflow-v0-19-0.md) | The same training choice retained again, including source signatures, 17 exact Applications, all 16 nested-source renders, public input OCI, ConfigHub variants, an approved release OCI, and the blocked EKS/H100 route. | Reading and local verification need no cloud account or GPU. Running it still needs EKS and H100 capacity. | AICR v0.19.0 | | [AICR v0.20.0 public starting configuration](./eks-h100-training-kubeflow-v0-20-0.md) | The same training choice on the newest retained version, including signed source provenance, 17 exact Applications, all 16 digest-bound nested renders, Argo CD and Flux lifecycle plans, and two public OCI artifacts. | Pulling, rendering, and inspecting it needs no ConfigHub account, cloud account, or GPU. ConfigHub variants, controller delivery, and H100 runtime remain separate work. | AICR v0.20.0 | | [The AICR-native NIM platform](./eks-h100-inference-nim.md) | A cluster that can serve NIM models. | AWS, GPU capacity, and NGC access to run the models. | AICR v0.14.0, `platform: nim` | | [NIM on KServe](./kserve-nim-inference.md) | The exact shape one model runs in, at model level rather than cluster level. | As above. | Upstream commit 3ef33472 | The training entry carries the proven mechanics. The two inference entries answer the serving question at different granularities: the AICR-native entry stands up a cluster that can serve NIM, and the KServe entry names the shape a given model runs in. The [inference entry](./eks-h100-inference-nim.md) compares them directly. ## What has actually been run Each entry says whether a check ran locally, on kind, against ConfigHub, or on a named destination. The retained work covers source verification, generation, rendering, digest pinning, variants, promotion, and selected delivery wiring. **No GPU workload ran to produce or verify any receipt in this catalog.** A local render or controller plan is not reported as a GPU result. The [CPU starter](./cpu-starter.md) has been taken furthest: imported into ConfigHub with a reviewed gp3 override, promoted to staging, delivered to a kind cluster, and one component synced to Healthy with its volume bound by the reviewed storage class. The training entry proved import, a reviewed Grafana change, and a staging promotion. The [inference entry](./kserve-nim-inference.md) proved two of those stages: import of all twenty-six retained surfaces with the license boundary held live, a reviewed model-cache rename that touched exactly the sixteen model shapes that mount it and left all ten serving runtimes untouched, and delivery of its serving surface to a kind cluster. What an entry has not done is listed per entry in the [platform evidence record](../../../data/aicr-platform-evidence/summary.md) rather than left to inference. Each receipt is recompiled offline by its own verify lane, so these are checkable rather than asserted. [The closing record](../../planning/aicr-track-conclusion.md) says what this track proves, what it refuses to claim, and what is left for whoever picks it up. ## Why the versions look old The training entry retains NVIDIA AICR v0.14.0 exactly: the release commit, the release-asset checksum, and the binary checksum are pinned in its [generation receipt](../../../examples/aicr/eks-h100-training-kubeflow/generation-receipt.yaml). How far behind upstream that leaves the catalog is measured rather than described. The [upstream watch](../../../data/aicr-upstream-watch/summary.md) compares every retained version against a committed snapshot of the release list, and it computes the release cadence instead of repeating it. That gap is the point of retention, not a defect. The retained entry stays byte-for-byte reproducible and its receipts stay true while upstream moves. Refreshing the catalog to a newer AICR version creates a new retained entry with its own receipts next to the old one; it never overwrites the proven shape. This is the same retained-versions discipline the chart catalog and the Kubara component catalog follow. That is no longer only a policy statement. The catalog retains [v0.18.0](./eks-h100-training-kubeflow-v0-18-0.md) and [v0.19.0](./eks-h100-training-kubeflow-v0-19-0.md), and [v0.20.0](./eks-h100-training-kubeflow-v0-20-0.md) beside the original entry. The [version comparison](../../../data/aicr-version-diff/summary.md) is rebuilt from their committed files. It shows which source versions, ordering rules, checks, and exact objects changed at each step. The v0.19.0 and v0.20.0 entries verify NVIDIA's signed CLI binary and recipe catalog against the exact release-workflow identity. Both keep the AICR recipe, generated source chart, literal Application OCI, route intent, and field-policy record connected instead of treating one rendered directory as the whole answer. Both entries now have digest-bound nested renders. The v0.20.0 entry also has separate Argo CD and Flux route resolutions, but neither has run on a target. The v0.19.0 entry continues further with ConfigHub variants and an approved ConfigHub release OCI. Those ConfigHub stages have not run for v0.20.0. ## How do I know these are the files AICR wrote? Every entry carries the digest spine the Kubara importer proved: one immutable OCI payload per component plus one digest-bound platform index. For the training entry, the [digest index](../../../examples/aicr/eks-h100-training-kubeflow/digest-index/README.md) pins the upstream source, the recipe criteria, the three OCI transport manifests, and all 17 rendered Argo CD Applications under a single platform digest. Change any rendered byte anywhere in the shape and the digest changes. ```bash npm run aicr-digest-index:verify npm run aicr-digest-index:self-test ``` The inference entry carries the same spine. Its [digest index](../../../examples/aicr/kserve-nim-inference/digest-index/README.md) pins the retained KServe tree, every serving runtime and model shape, and the described model profile, and its compiler adds the licensing boundary as refusal rules: gated images stay references, and a literal credential value anywhere in the tree stops the compile. ```bash npm run aicr-kserve-nim:verify npm run aicr-kserve-nim:self-test ``` The CPU starter's index adds a derivation chain: each member payload records the training-entry payload hash it copies and the training entry's platform digest, so the path from the AICR release pins through the training index to the starter is checkable end to end. ```bash npm run aicr-cpu-starter:verify npm run aicr-cpu-starter:self-test ``` The training entry also carries an OCI round-trip receipt and a variant-promotion receipt that changed one Grafana credential in a development variant and promoted it to staging with matching data hashes. The [entry page](./eks-h100-training-kubeflow.md) walks through both. ## What this catalog turns down The catalog refuses manufactured variety. A new entry comes from an upstream version, from a deliberately authored shape whose provenance is named, or from a recorded derivation of an entry that already exists. The inference entry's sixteen model-by-GPU shapes are upstream-authored variety retained exactly, and the starter's seven components are byte-identical copies selected by rules its derivation receipt records. The [refusal corpus](./refusal-corpus.md) records what it turns down, by running the shipped lanes against changes a contributor could plausibly propose and publishing the verdicts. Two of those candidates have to be accepted, because lanes that refused everything would look identical to lanes that refused the right things. The counts on these pages are checked the same way. Every number followed by a counted noun is bound to a quantity computed from committed bytes, and [the claim-integrity lane](./claim-integrity.md) refuses both a number that has drifted and a counted claim nobody declared.