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What does Bob run on?
Bob’s backend is a customer-managed workload on OCP. IBM lists OCP versions 4.20, 4.21, and 4.22 as supported. Bob workloads must run on amd64/x86_64 workers. A mixed-architecture cluster can work, but operators must constrain Bob workloads to amd64 nodes; Bob does not add those scheduling constraints automatically. See IBM’s system requirements and deployment overview.
Bob’s resource figures are not a complete OpenShift cluster specification. They describe aggregate Bob tenant workloads; platform services, high availability, other tenants, and growth must be accounted for separately.
How much CPU, RAM, and storage should you plan for?
IBM’s stack table gives raw aggregate workload figures for Bob Core and optional add-ons. Its Z Understand figures are provisional because benchmarking is in progress.
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| Deployment stack | CPU | Memory | Persistent volumes | Status |
|---|---|---|---|---|
| Bob Core | 22.1 vCPU | 35.1 GiB | About 30 GiB | Baseline available |
| Bob Core + RAG | 38.1 vCPU | 69.1 GiB | About 62 GiB | Baseline available |
| Bob Core + Z Understand | 30.1 vCPU | 74.1 GiB | About 2,288 GiB | Provisional; benchmarking in progress |
| Bob Core + RAG + Z Understand | 46.1 vCPU | 108.1 GiB | About 2,320 GiB | Provisional; benchmarking in progress |
For production Bob Core planning, IBM separately specifies a raw profile of 28.1 vCPU, 41.1 GiB RAM, and about 50 GiB of persistent volumes. It recommends 25–30% headroom for CPU and memory, yielding about 36.5 vCPU and 53.4 GiB RAM. The roughly 50 GiB storage figure is not increased by that headroom calculation. These production figures and the stack-table values serve different planning views; use the production profile with headroom when sizing worker capacity. Refer to IBM’s system requirements.
What does IBM’s OpenShift reference topology include?
IBM’s minimum dedicated-cluster reference has nine nodes. Its worker pool is the relevant place to check whether Bob’s workload can fit; the listed node sizes are not a substitute for checking actual allocatable capacity in your own cluster.
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| Node role | Count | Reference size per node | Notes |
|---|---|---|---|
| Control plane | 3 | 4 vCPU, 16 GiB RAM | Cluster control-plane capacity |
| Infrastructure | 3 | About 4 vCPU, 16 GiB RAM | Infrastructure services |
| Worker | 3 | 20 vCPU, 24 GiB RAM, 200 GiB local storage | 600 GiB worker storage total |
The reference configuration totals about 84 vCPU, 168 GiB RAM, and 600 GiB of worker storage. After OpenShift overhead, IBM estimates the workers provide around 57 vCPU and 63 GiB of allocatable capacity—enough for the headroom-adjusted Bob Core profile. A dedicated cluster is not mandatory when a shared OCP cluster has adequate capacity. IBM’s system requirements provide the reference topology and overhead estimate.
Does self-hosted Bob need GPUs?
Not for the Bob backend itself, based on IBM’s published requirements. Bob connects to deployed models through its Model Inference Gateway; it does not provision, host, or manage model-serving infrastructure. Therefore, do not add a presumed GPU count to Bob’s backend CPU and RAM budget. GPU and VRAM needs belong to the separately operated inference service and depend on the model and workload. IBM describes the gateway and supported-model considerations in its required and supported models documentation.
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IBM identifies factors that affect serving capacity, including model quantization, context length, serving runtime such as vLLM or TGI, concurrency, and target throughput. Its documentation references models including Mistral 3.5, NVIDIA Nemotron 3, and Poolside Laguna S2.1. IBM’s October 1, 2026 release article identifies NVIDIA Nemotron 3 Ultra and Poolside Laguna S 2.1 for the disconnected route. Model compatibility and vendor hardware guidance can change, so verify both for the specific deployment before selecting GPUs. See IBM’s model documentation and its September 2026 release article.
IBM’s October 1, 2026 article says self-hosted deployment became generally available on September 24, 2026. It also states that hardware sizing follows each model vendor’s guidance because it depends on quantization, context length, and how many developers are served at once. No universal GPU model or count is specified in the cited IBM guidance.
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Can you use a cloud model instead of hosting GPUs?
Yes. IBM’s guidance supports three broad arrangements, provided the model endpoint is reachable from the Bob cluster and conforms to the serving requirements:
- On-cluster serving: Use OpenShift AI or another serving platform for self-hosted models, including air-gapped deployments.
- Private infrastructure: Connect Bob to GPU servers or an inference cluster operated separately from the Bob backend.
- Cloud inference: Connect to a provider endpoint such as AWS Bedrock, Azure OpenAI, or Google Vertex AI.
The Model Inference Gateway is designed to connect to deployed models through an OpenAI-compatible API. A cloud endpoint can therefore avoid customer-managed inference GPUs, while an air-gapped deployment requires a separately sized serving tier. IBM’s model guidance and deployment overview describe these boundaries.
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What storage and installation prerequisites matter?
Bob uses both ReadWriteOnce (RWO) and ReadWriteMany (RWX) volumes. IBM identifies Managed NFS and OpenShift Data Foundation (Ceph-backed RBD and CephFS) as supported storage classes. PostgreSQL, OpenSearch, and Redis use RWO volumes; shared configuration and certificates require RWX.
- Use SSD-backed block storage for PostgreSQL and high-performance block storage for OpenSearch, as IBM recommends.
- Check throughput and I/O performance, not just capacity: IBM warns that insufficient performance—especially for PostgreSQL—can increase response times, slow indexing, and reduce stability.
- Do not treat the 600 GiB worker-storage figure in the topology as Bob-only storage; it also accommodates platform services and growth.
Installation also needs an administrative workstation with network access to the cluster, the release bundle, access to IBM’s entitled container registry, and cluster-admin or equivalent permissions for cluster-scoped resources. The workstation is an installation tool; IBM does not specify a special GPU workstation for the Bob backend. See IBM’s system requirements and installation prerequisites.
How to turn the requirements into a sizing plan
- Choose the Bob stack. Start with Core or include RAG; treat Z Understand storage and capacity values as provisional until IBM completes benchmarking.
- Check worker capacity. Compare the headroom-adjusted Bob Core production profile with worker allocatable CPU and memory, not just node totals. Include OpenShift overhead, other tenants, high availability, and expected growth.
- Validate storage design. Confirm the required RWO and RWX access modes, volume capacity, and suitable I/O performance for the database and search services.
- Choose the inference arrangement separately. Decide whether the endpoint is on-cluster, private, or cloud-hosted, then select a supported model and runtime.
- Size and test model serving. Use the model vendor’s guidance for quantization, context length, concurrent demand, and throughput; capacity-test the inference tier rather than assuming a fixed GPU count.
IBM’s model guidance makes inference sizing workload-dependent, while its system requirements define the Bob and OpenShift planning figures.
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