Minimum Hardware Requirements

Minimum CPU, memory, storage, and GPU capacity needed to run the BullSequana AI platform.

This page defines the minimum hardware baseline for a full BullSequana AI platform deployment.

Treat this baseline as the practical floor for getting started with BullSequana AI. Scale beyond it as production usage grows.

Minimum Cluster Sizing

The complete platform is validated on the following cluster shape:

Node pool	Nodes (min–max)	vCPU per node	RAM per node	Pool total (minimum)
System	2	4	16 GiB	8 vCPU / 32 GiB
Workers	7–10	8	32 GiB	56 vCPU / 224 GiB

That gives a cluster minimum of:

64 vCPU / 256 GiB RAM at the minimum worker count (7 workers)
72 vCPU / 288 GiB RAM at the typical initial worker count (8 workers)

Only the aggregate capacity and the per-node shape matter. Nodes with more vCPU and RAM per node are equally valid as long as the cluster totals are met and no single node is smaller than the worker shape above.

How The Platform Uses This Capacity

At steady state the platform reserves about 36 cores and 86 GiB of RAM, and can consume up to roughly 95 cores and 169 GiB under load:

Platform layer	CPU (steady state)	CPU (under load)	RAM (steady state)	RAM (under load)
Cluster infrastructure	9 cores	24 cores	28 GiB	43 GiB
CoreAI serving	17 cores	55 cores	42 GiB	111 GiB
Data and ML	11 cores	15 cores	16 GiB	16 GiB
Total	~36 cores	~95 cores	~86 GiB	~169 GiB

The cluster minimum above is intentionally larger than the steady-state figures. The headroom covers load peaks, background jobs, rolling upgrades, and continued operation when a node is unavailable.

Storage

The platform provisions roughly 1.2 TiB of persistent storage:

Consumer	Storage
Strimzi (Kafka)	300 GiB
Rook-Ceph cluster backing	260 GiB
MLflow	210 GiB
Grafana	100 GiB
Milvus	~97 GiB
Other components	~200 GiB

Plan at least 1.5–2 TiB of usable storage to leave operational headroom. If Ceph replication is configured above a replication factor of 1, multiply the raw disk requirement accordingly.

The cluster must expose valid RWO and RWX storage classes before deployment. See Prerequisites for the storage-class inputs.

GPU Requirements

Every GPU workload in the platform requests exactly one full GPU per replica. The platform does not configure GPU slicing (no MIG or time-slicing), so each served model occupies a whole GPU.

The minimum GPU footprint for CoreAI inference is 3 GPUs:

Workload	Model	GPUs	Host resources per GPU
LLM serving (KubeAI / vLLM)	selected at runtime	1	~6 vCPU / 24–48 GiB depending on GPU class
Speech-to-text (Speaches)	whisper-large-v3-turbo	1	1–4 vCPU / 2–8 GiB, plus a 10 GiB model-cache volume
Embedding (KubeAI)	selected at runtime	1	~6 vCPU / 24 GiB

Sizing guidance:

the speech-to-text and embedding models run comfortably on a T4 or L4 class card
the LLM card should be sized to the largest model you intend to serve; an L40S or A100 80 GB class card covers mid-size models
KubeAI ships resource profiles from T4 and RTX 4070 8 GB up to H200 and GH200, so larger cards are supported without configuration changes

An optional fourth GPU can be assigned to Docling for document processing. It defaults to CPU mode.

GPU node configuration

GPU scheduling is driven by node metadata, not by VM SKU. GPU nodes must provide:

node label workload=gpu
node taint nvidia.com/gpu=present
the nvidia runtime class, with NVIDIA drivers and device plugin installed at cluster level

By default the platform uses preferred (soft) GPU affinity. Set common_enforce_affinity to true to make GPU placement a hard scheduling requirement.

Summary

Dimension	Minimum
CPU	64 vCPU across 9 nodes (72 vCPU recommended)
RAM	256 GiB (288 GiB recommended)
Storage	1.5–2 TiB usable, with `RWO` and `RWX` storage classes
GPUs	3 × dedicated GPUs (LLM, speech-to-text, embedding), 1 optional for Docling