High-Performance Training, Low-Latency Inference, Audit-Ready Ops
Bare Metal & GPU Compute gives you dedicated CPUs/GPUs with direct access to accelerators and fabrics (e.g., NVLink, InfiniBand, RoCE) for AI/ML training, inference, HPC, and graphics workloads.
SolveForce designs clusters that are secure-by-default, data-fast, scheduler-aware, and cost-smart—on-prem, in colocation, and in public cloud—wired to identity, keys, network on-ramps, and evidence.
Where this fits:
☁️ Platform → Cloud • 🏢 Hubs → Colocation • 🔗 On-ramps → Direct Connect
☸️ Orchestration → Kubernetes • 🧠 AI/RAG → Vector Databases & RAG • 📚 AI Knowledge Standardization
🔑 Keys/Secrets → Key Management / HSM • Secrets Management • Encryption
📊 Evidence/Automation → SIEM / SOAR • 🛠️ Pipelines → DevOps / CI-CD • 💸 Cost → FinOps
🎯 Outcomes (Why SolveForce Bare Metal & GPU)
- Throughput up — fast interconnects, pinned NUMA, and storage pipelines keep GPUs busy.
- Time-to-train down — NCCL-aware topologies, job packing, mixed precision (FP16/BF16/FP8).
- Low-latency inference — tuned kernels, KV cache, MIG partitioning, autoscaling.
- Secure multi-tenant — isolation (MIG/SR-IOV), secrets in vault, per-tenant slices and quotas.
- Audit-ready — job logs, artifacts, approvals exported to SIEM; cost and SLO dashboards.
🧭 Scope (What we build & run)
- Nodes — GPU (A100/H100/L40S/MI300-class), CPU (x86/ARM), NVMe tiers, high-RAM SKUs.
- Fabrics — InfiniBand (HDR/NDR), Ethernet RoCEv2 (25/50/100/200/400G), NVLink/NVSwitch inside nodes.
- Storage — NVMe pools, parallel filesystems (Lustre/GPFS), S3-compatible object, RDMA-enabled caches.
- Schedulers — Kubernetes (Device Plugin/MIG), Slurm, Ray, Airflow for workflows.
- MLOps — model registry, artifact stores, feature stores, CI-CD for training/inference. → DevOps / CI-CD • Vector Databases & RAG
- Security & keys — CMK/HSM signing/encryption for models/checkpoints; short-lived tokens. → Key Management / HSM • Secrets Management
🧱 Architecture Building Blocks (Spelled out)
- Topology — leaf/spine or dragonfly with ECMP; keep 1–2 oversubscription for training pods; pin NCCL rings to physical layout.
- Compute isolation — MIG (Multi-Instance GPU) for hard partitions; SR-IOV for NICs; CPU pinning & hugepages.
- Network — RDMA for all-reduce; DCB/PFC + ECN for RoCE; QoS lanes for storage vs control.
- Storage path — NVMe scratch → hot cache → parallel FS or object store; checkpoint streams with large sequential IO; GDS (GPUDirect Storage) where supported.
- Data ingress — private on-ramps (Direct Connect/ExpressRoute/Interconnect), WAN QoS, pre-stage datasets in colo to cut egress/latency. → Direct Connect • Colocation
🛠️ Patterns (Choose your fit)
A) Distributed Training Cluster
- InfiniBand + NCCL; 8–16+ GPUs/node; NVLink/NVSwitch internal.
- Slurm or Kubernetes with gang scheduling; mixed precision; gradient checkpointing; async data loaders.
- Snapshot/Resume to immutable storage; Object Lock for ransomware safety. → Backup Immutability
B) Inference Autoscaling (Low Latency)
- Kubernetes + GPU operator; horizontal pod autoscaler; MIG for right-size shards; Triton/TensorRT/ONNXRuntime.
- KV cache & paged attention; CPU offload for non-critical ops; cold/warm pools.
C) Hybrid Burst (On-prem ↔ Cloud)
- Baseline on colo/on-prem; burst to cloud using identical images; artifact registry sync & keys via vault; cost guardrails. → FinOps
D) ETL → Feature → Train → Serve
- Data lake → curated features; training on GPU nodes; registry → rollout via CI-CD; guarded RAG with vector DB. → Data Warehouse / Lakes • ETL / ELT
🔒 Security & Zero-Trust (Concrete, enforceable)
- Identity — SSO/MFA for users; JIT/PAM for admin; per-namespace/queue RBAC. → IAM / SSO / MFA • PAM
- Secrets/keys — models/checkpoints signed + encrypted; short-lived tokens; no plaintext in code/images. → Secrets Management • Key Management / HSM
- Boundary — ZTNA for consoles; WAF/Bot for APIs; origin cloaking with mTLS. → ZTNA • WAF / Bot Management
- Data privacy — DLP labels; PII kept out of scratch; field-level encryption where required. → DLP • Encryption
- Evidence — scheduler events, model lineage, and job artifacts → SIEM; SOAR performs safe revoke/rollback. → SIEM / SOAR
📐 SLO Guardrails (Experience & capacity you can measure)
| SLO / KPI | Target (Recommended) |
|---|---|
| GPU Utilization (cluster avg) | ≥ 70–85% training • ≥ 40–70% inference |
| Queue wait (p95, scheduled jobs) | ≤ 5–15 min (policy dependent) |
| Throughput gain (A/B) | ≥ 15–30% after topology/cache tuning |
| Job success (rolling 30d) | ≥ 98–99% (excl. preemptions) |
| Network fabric saturation (p95) | < 70–80% sustained during all-reduce |
| Storage throughput (per node) | ≥ 5–20+ GB/s sequential (scratch/cache) |
| Evidence completeness | 100% (jobs, artifacts, approvals, lineage) |
SLO breaches trigger SOAR actions (re-queue, scale-out, route adjust, rollback) and open tickets. → SIEM / SOAR
💰 FinOps for GPUs (Cost that behaves)
- Right-size: match model to MIG profile or GPU class; avoid overspec.
- Pack jobs: gang scheduling; bin-packing by mem/SM; spot/preemptible where safe.
- Mixed precision: BF16/FP16/FP8; flash attention; quantized inference (INT8/FP8).
- Checkpointing: resume long runs; avoid lost epochs on preempt.
- Data locality: pre-stage datasets in colo; minimize cross-region egress; cache hot shards.
- Power/thermals: track W/TFLOP; cap clocks when I/O-bound.
→ Guardrails & dashboards in FinOps. FinOps
📊 Observability
- GPU: util/mem/SM occupancy, power, thermals, ECC, MIG layout.
- NCCL: all-reduce time, imbalance, link errors.
- Network: RDMA counters, PFC/ECN marks, retransmits.
- Storage: read/write GB/s, IOPS, tail latency, cache hit rate.
- Scheduler: queue wait, preemptions, retries, fairness.
- Cost: $/GPU-hr, $/1K inferences, $/epoch, $/TB scanned.
All exported to SIEM and FinOps boards; alerts drive SOAR playbooks. → SIEM / SOAR
📜 Compliance Mapping (Examples)
- PCI DSS — key custody for model/artifacts, WAF logs for API endpoints.
- HIPAA — PHI controls, audit trails, immutable logs, encrypted artifacts.
- ISO 27001 — ops security, access, change evidence.
- NIST 800-53/171 — AC/SC/CM controls; boundary & crypto.
- CMMC — enclave separation, logging, retention.
Artifacts (lineage, signatures, approvals) are exportable for auditors.
🛠️ Implementation Blueprint (No-Surprise Rollout)
1) Workload inventory — training vs inference; models, params, datasets, latency targets.
2) Site & fabric — InfiniBand vs RoCE; leaf/spine; ECMP; NVLink topology.
3) Nodes & storage — GPU class, NVMe tiers, parallel FS/object; GDS where supported.
4) Scheduler — K8s Device Plugin/MIG or Slurm; gang scheduling; quota & fairness.
5) Security — SSO/MFA, ZTNA, vault, CMK/HSM, WAF; DLP labels on datasets.
6) Pipelines — CI-CD for train/serve; signed artifacts; model registry.
7) SLOs & dashboards — utilization, queue wait, fabric/storage health, cost.
8) DR/backup — immutable checkpoints, artifact registry backups; restore drills. → Cloud Backup • Backup Immutability
9) Operate — weekly posture & cost reviews; quarterly perf tune; publish RCAs & wins.
✅ Pre-Engagement Checklist
- 🎯 Models/workloads, epochs, batch sizes, latency/throughput goals.
- 🖧 Fabric choice (IB/RoCE), ports/speeds, NVLink presence, topology maps.
- 🖥️ GPU types/MIG needs; CPU/RAM ratios; NVMe capacity.
- 🗃️ Storage (scratch/cache/parallel FS/object), GDS readiness.
- ☸️ Scheduler (K8s/Slurm/Ray), quotas, preemption policy.
- 🔐 Identity/keys/secrets, ZTNA/WAF posture, DLP policy for datasets.
- 🔗 On-ramps (Direct Connect/ExpressRoute/Interconnect), colo presence.
- 📊 SLO & FinOps targets; SIEM/SOAR integration; evidence format.
🔄 Where Bare Metal & GPU Compute Fits (Recursive View)
1) Grammar — runs on Connectivity & Networks & Data Centers.
2) Syntax — provisioned via Cloud and Infrastructure as Code; orchestrated by Kubernetes or Slurm.
3) Semantics — Cybersecurity preserves truth; keys/secrets prove custody.
4) Pragmatics — SolveForce AI learns from telemetry and suggests pack/topology/cache optimizations.
5) Foundation — consistent terms via Primacy of Language.
6) Map — indexed in the SolveForce Codex & Knowledge Hub.
📞 Build GPU Clusters That Are Fast, Secure & Auditable
Related pages:
Cloud • Colocation • Direct Connect • Kubernetes • Vector Databases & RAG • AI Knowledge Standardization • Data Warehouse / Lakes • ETL / ELT • DevOps / CI-CD • FinOps • Encryption • Key Management / HSM • Secrets Management • SIEM / SOAR • Cybersecurity • Knowledge Hub