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.
- Slurmor Kubernetes with gang scheduling; mixed precision; gradient checkpointing; async data loaders.
- Snapshot/Resumeto 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-sizematch model to MIG profile or GPU class; avoid overspec.
- Pack jobsgang scheduling; bin-packing by mem/SM; spot/preemptible where safe.
- Mixed precisionBF16/FP16/FP8; flash attention; quantized inference (INT8/FP8).
- Checkpointingresume long runs; avoid lost epochs on preempt.
- Data localitypre-stage datasets in colo; minimize cross-region egress; cache hot shards.
- Power/thermalstrack W/TFLOP; cap clocks when I/O-bound.
→ Guardrails & dashboards in FinOps. FinOps
📊 Observability
- GPUutil/mem/SM occupancy, power, thermals, ECC, MIG layout.
- NCCLall-reduce time, imbalance, link errors.
- NetworkRDMA counters, PFC/ECN marks, retransmits.
- Storageread/write GB/s, IOPS, tail latency, cache hit rate.
- Schedulerqueue 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
🔄 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
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Cybersecurity
The practices and controls used to protect identities, devices, networks, applications, and data from unauthorized access, disruption, or manipulation.
Zero Trust
A security model that does not automatically trust a user or device because of its location. Access is continuously verified and limited to what is necessary.
Identity and Access Management (IAM)
The systems and policies that determine who a user is, what resources they may access, and how that access is authenticated and reviewed.
Multi-Factor Authentication (MFA)
A login control requiring more than one form of verification, such as a password plus an authenticator app, security key, or biometric factor.
Colocation
Placing customer-owned servers and network equipment in a professionally operated data center that provides power, cooling, physical security, and connectivity.
API
An application programming interface is a defined way for software systems to exchange data or request functions from one another.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.