Documentation – OpenMetal Cloud

We look at why financial services firms accumulate large, long-lived data retention and audit archive requirements, why hyperscaler storage pricing works against that access pattern specifically, and how a large-scale Ceph cluster handles the same requirement with predictable costs and full control.

We answer a specific legal question that general Singapore sovereignty content doesn’t: whether US CLOUD Act jurisdiction reaches infrastructure physically hosted in Singapore, how that’s separate from Singapore’s own PDPA framework, and what that means if you’re evaluating a US-owned infrastructure provider for APAC deployment.

We explain why AI agents that execute code need microVM-level isolation, why that isolation requires direct hardware access that public cloud VMs can’t provide, and how self-hosting a Firecracker or Kata sandbox on dedicated bare metal compares to managed platforms like E2B on cost and control.

We explain what Intel TDX confidential computing actually protects, confirm which hardware configuration delivers it in our Amsterdam data center today, and walk through why pairing TDX with EU data residency matters for regulated workloads.

Coding-agent fleets hit dedicated-GPU break-even at ~5-10M tokens/month or 15-25% utilization. Why metered per-token billing punishes the agent workload.

We compare Amsterdam against Frankfurt, Dublin, and Paris as EU infrastructure locations, covering network connectivity, latency to key regions, and data residency considerations, then explain why Amsterdam is where OpenMetal actually operates.

Neocloud became a capital-and-power race, leaving sustained mid-market inference underserved. Why predictable cost, not GPU count, is the defensible position.

We break down the real difference between data residency and data sovereignty, why many “sovereign cloud” claims from US-owned providers don’t hold up under scrutiny, and what EU-based infrastructure can and can’t actually guarantee.

We look at why Azure’s egress fees are no longer the sharpest lock-in mechanism, and walk through the specific managed services (Azure Functions, Cosmos DB, Service Bus, Logic Apps, Azure AD B2C / Entra External ID) that actually make leaving Azure hard, including the one place Azure is more open than either AWS or GCP, and the one place it’s arguably worse.

Prefill is compute-bound, decode is memory-bandwidth-bound. Why splitting inference into two purpose-fit GPU pools beats one uniform fleet.

After weights load, the HBM left over is your KV-cache budget. Why the H200’s 141GB buys more context and concurrency than a 94GB H100.

Map MongoDB, Redis, Kafka, ClickHouse, and Kubernetes workers to OpenMetal SKUs by the resource each role saturates, then size the failure domain.

Check out what’s new with OpenMetal Central and our cloud management and control capabilities in July 2026.

We look at why Google Cloud’s egress fees are no longer the sharpest lock-in mechanism, and walk through the specific managed services (Cloud Functions, Firestore, Cloud Spanner, Pub/Sub, Cloud Workflows, Identity Platform) that actually make leaving Google Cloud hard, including where GCP’s lock-in profile is genuinely different from AWS’s.

We look at why AWS egress fees are no longer the lock-in mechanism people think they are, and walk through the specific managed services (Lambda, DynamoDB, Step Functions, EventBridge, SQS/SNS, Cognito, API Gateway) that actually make leaving AWS hard.

Yes, Llama 3.3 70B runs on a single OpenMetal H200 at FP8 with full 128K context. See the VRAM fit math, KV-cache budget, and vLLM setup.

An ordered Day-2 playbook for a single-tenant H200: full root and IPMI, owning the CUDA stack, boot-data isolation, and a node-bounded blast radius.

The operator trust problem in MEV block building has a hardware solution. This article explains why Intel TDX has become the substrate of choice for confidential block building, and what bare metal adds that cloud TDX doesn’t.

Planning a private cloud project? Organizing well from the start can prevent expensive and time-consuming delays. Our guide explains why hosted private cloud projects stall across migration and day two operations and shows how to prevent delays with better planning, architecture, ownership, and operational readiness.

OpenMetal XL v5 keeps 64 cores but changes node, memory, I/O, power, AMX, and TDX readiness. Where v5 wins, and the one spec that regresses.

OpenMetal XL v5 vs XL v4: same 64 cores, but v5 adds 33% memory bandwidth, more PCIe lanes and drive bays, and CPU-side AI; v4 keeps more L3 cache.

A skimmable breakdown of the main business and technical drivers pushing companies from public cloud to hosted private cloud, covering cost control, compliance, performance, and operational control.

Healthcare AI workloads carry the same HIPAA obligations as any system touching PHI. This article covers what the 2026 Security Rule update requires from AI infrastructure, why vector embeddings count as PHI, and how dedicated private cloud simplifies the compliance documentation burden.

Hyperscaler credits are worth taking, but the architecture built during the subsidized period determines your real cost when billing starts. This covers the credit lifecycle, which decisions create long-term cost exposure, and when private infrastructure makes sense for AI startups in production.

Nutanix is a legitimate VMware alternative with real advantages. But its per-core subscription model has cost implications that compound at scale. This article compares both platforms honestly across pricing, operations, migration tooling, and use case fit.

Azure CSP resale margins are thin and getting thinner as Microsoft shifts incentives away from transaction volume. This article covers the commercial model for MSPs who own their infrastructure instead, how OpenStack multi-tenancy enables per-client isolation on shared hardware, and what the right client segment looks like.

The H200 is a memory upgrade on the Hopper architecture, not a new compute platform. This article covers why bandwidth matters as much as VRAM capacity, where the 141GB floor changes what fits on a single GPU, and how the NVL PCIe variant differs from the SXM5 for dedicated private infrastructure.

Databricks’ Standard tier is being retired, forcing Premium upgrades with higher DBU rates. This article covers how the DBU billing model works, what the open-source stack underneath Databricks looks like, what you give up by self-managing it, and when private cloud infrastructure changes the economics.

The RTX PRO 6000’s 96GB VRAM fits 70B models at FP8 on a single card with real KV cache headroom. This article covers what that unlocks, how dedicated fixed-cost GPU infrastructure compares structurally to cloud rental, and where the H200 is the better choice.

NVIDIA H200 vs H100 for AI training and inference: 141GB HBM3e vs 80–94GB, same Hopper compute with more memory. OpenMetal runs the H200 on bare metal.

NVIDIA RTX Pro 6000 vs H200 on OpenMetal: 96GB GDDR7 + FP4 for cost-efficient AI vs 141GB HBM3e for the largest models. Both single-tenant bare metal.

OpenMetal NVIDIA H200 bare metal GPU server: 141GB HBM3e, dual Xeon 6530P, 1TB DDR5. Single-tenant bare metal, fixed monthly pricing.

OpenMetal GPU clusters: dedicated single-tenant multi-GPU infrastructure. All-RP6000, all-H200, or mixed on a private 40 Gbps mesh, fixed monthly pricing.

OpenMetal NVIDIA RTX Pro 6000 GPU server: 96GB GDDR7, FP4, dual Xeon 6530P, 1TB DDR5. Training and inference, single-tenant, fixed monthly pricing.

Q: What is the difference between the NVIDIA RTX PRO 6000 and H100? The RTX PRO 6000 is a Blackwell GPU with 96GB of GDDR7 and native FP4, while the

Q: Is the RTX PRO 6000 better than the L40S for AI inference and training? For most training and inference the RTX PRO 6000 outperforms the L40S on a single

Add NVIDIA RTX Pro 6000 or H200 GPU servers to an existing OpenMetal cloud or bare metal deployment – same private network, fixed monthly pricing.

NVIDIA RTX Pro 6000 vs H100: specs, cost, deployment fit. 96GB GDDR7 + FP4 vs 80–94GB HBM3. OpenMetal offers the RP6000 and H200 on bare metal.

NVIDIA RTX Pro 6000 vs L40S for AI training and inference: 96GB GDDR7 + FP4 (Blackwell) vs 48GB GDDR6 (Ada). OpenMetal runs the RP6000 on bare metal.

Q: Can I attach NVIDIA RTX PRO 6000 GPU nodes to an existing OpenMetal bare metal or Hosted Private Cloud deployment? Yes, you can attach RTX PRO 6000 GPU nodes

Q: What is the difference between the NVIDIA RTX PRO 6000 and L40S? The RTX PRO 6000 is a newer Blackwell-generation GPU with 96GB of GDDR7 and native FP4, while

Q: Can I build a mixed GPU cluster with NVIDIA RTX PRO 6000 and H200 servers? Yes, OpenMetal builds mixed GPU clusters that combine RTX PRO 6000 and H200 nodes

Q: What is FP4 (NVFP4) and why does it matter for AI workloads? FP4 (NVFP4) is a Blackwell-native 4-bit floating-point format that increases low-precision inference throughput beyond the FP8 ceiling

Q: How does OpenMetal’s fixed-cost GPU pricing avoid the cloud “idle silicon tax”? OpenMetal charges a fixed monthly rate for a dedicated GPU server, so running the card at 100%

Q: Can I train and fine-tune AI models on the OpenMetal RTX PRO 6000, or is it only for inference? Yes, the OpenMetal RTX PRO 6000 trains and fine-tunes AI

Q: How much GPU memory does the OpenMetal RTX PRO 6000 have? Each OpenMetal RTX PRO 6000 GPU carries 96GB of GDDR7 memory, and a server can hold one or

Q: GDDR7 vs HBM3: which matters for AI training and inference? GDDR7 offers high capacity at lower cost, while HBM3/HBM3e delivers much higher memory bandwidth; bandwidth is what matters most

Q: Can I run a 70B parameter LLM on a single OpenMetal H200? Yes, a single OpenMetal H200 runs a 70B-parameter model in 16-bit precision, because its 141GB of HBM3e

Q: Can I build a multi-GPU cluster with OpenMetal H200 servers? Yes, OpenMetal builds dedicated multi-GPU clusters of H200 servers on a private mesh, built to order for distributed training

Q: Can I add GPU servers to my existing OpenMetal cloud or bare metal deployment? Yes, you can add NVIDIA RTX PRO 6000 or H200 GPU servers to an existing