The answer is set by three things: the model’s weight footprint, the KV cache your context and concurrency produce, and how your serving framework spreads both across GPUs. An eight-GPU
Category: GPU Servers & Clusters
The NVIDIA HGX B300 is NVIDIA’s Blackwell Ultra platform in its densest single-node form: eight B300 SXM GPUs wired together on one baseboard by fifth-generation NVLink and NVSwitch into a
A practical guide to matching NVIDIA’s H100, H200, B200, and B300 to your model, your latency target, and your budget, starting from the workload instead of the spec sheet. Key
NVIDIA AI Enterprise (NVAIE) is a support and integration product, and knowing that up front settles most of the buying decision. It does not unlock faster GPUs or a private
An account-intelligence system that pre-embeds ten million companies into a resident vector index, and one that dispatches agents to research those same companies live on demand, look like the same
On latency-bound inference, the Model FLOPs Utilization your optimization stack can actually hold is capped by who else shares the box, not by the kernel that runs on it. Sustained
A first-party FP4 checkpoint moves GPU selection from memory capacity to native tensor-core format support, and inverts the usual verdict. Based on the Inkling-Small.
Claude is closed-weight and cannot run on your own hardware, but you can self-host the entire application and data plane around it on OpenMetal. Here is how.
Gemini is closed and cannot run on your own hardware, but you can self-host the entire application and data plane around it on OpenMetal. Here is how.
GPT is closed-weight and cannot run on your own hardware, but you can self-host the entire application and data plane around it on OpenMetal. Here is how.
Claude, GPT, and Gemini cannot run on hardware you own, but you can self-host the entire stack around them on OpenMetal. Here is what is actually possible.
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.
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.
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.
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
Q: What NVMe storage does the OpenMetal H200 GPU server use? The OpenMetal H200 GPU server uses a 6.4TB Micron 7500 MAX NVMe SSD for data, plus two 960GB NVMe
Q: What CPU is paired with the OpenMetal H200 GPU server? Each OpenMetal H200 GPU server pairs the GPU with two Intel Xeon 6530P processors (Granite Rapids), giving 64 cores
Q: Should I choose the NVIDIA RTX PRO 6000 or the H200 for my workload? Choose the RTX PRO 6000 for cost-efficient training, fine-tuning, and high-throughput inference that fit in
Q: How does OpenMetal GPU pricing compare to AWS GPU instances? OpenMetal prices GPU servers on a fixed monthly model with included egress, while AWS bills GPU instances per GPU-hour
Q: What is the difference between the NVIDIA H200 and H100? The H200 and H100 share the same Hopper compute architecture; the H200’s advantage is memory, with 141GB of HBM3e
Q: Is the NVIDIA H200 faster than the H100 for AI inference? For memory-bound LLM inference, yes: the H200’s higher HBM3e bandwidth (4.8 TB/s vs 3.35-3.9 TB/s) directly raises tokens-per-second,
Q: Why does OpenMetal offer the NVIDIA H200 instead of the H100? OpenMetal carries the H200 rather than the H100 because the H200 is the H100’s direct successor: 50% more