HGX B200 brings 192GB of HBM3e per GPU, FP4 Transformer Engine v2, and 5th-gen NVLink at 1.8 TB/s. Reserve 2026 allocation today — Blackwell supply is gated and prioritized by commitment.
Native 4-bit floating point with auto-scaling — up to 15x H100 inference on large transformer models.
1.5 TB HBM3e in an 8-GPU node. Run 405B-class models in higher precision with full KV cache headroom.
Double H100 NVLink bandwidth. Tensor-parallel scaling stays linear well past 8 GPUs in a single domain.
If you're training 500B+ parameter models, Blackwell is the platform — and getting allocation in 2026 requires reserving now.
Quantization to FP4 with no accuracy loss on most LLMs gives 4x serving density vs H100 FP8.
Your H100 CUDA code runs on B200. Migrate when allocation arrives — no architectural rewrite.
Blackwell was designed for the next generation of foundation models — multi-trillion-parameter dense transformers, sparse MoE architectures with hundreds of experts, and long-horizon RL post-training. 5th-gen NVLink at 1.8 TB/s keeps tensor-parallel groups linear well past a single 8-GPU domain, and NVL72 rack-scale options extend that to 72 GPUs in one coherent memory pool.
1.5 TB of HBM3e per 8-GPU node fits 1T+ parameter models in FP8 without offload. FP4 doubles that effective capacity again for inference.
Wider expert routing benefits directly from doubled NVLink bandwidth. All-to-all collectives that bottlenecked on H100 disappear on Blackwell.
Reward-model + policy + reference all resident in HBM. PPO/GRPO rollouts no longer dominated by parameter shuffling.
Auto-scaling FP4/FP6/FP8 with no accuracy loss on most architectures — typical 2.5x training throughput over H100 on transformer workloads.
Production AI in 2026 is dominated by agentic loops, reasoning models that emit thousands of tokens per request, and multimodal serving. FP4 with Transformer Engine v2 gives Blackwell up to 15x H100 inference throughput on large transformers — turning $/Mtok economics on its head.
o1/R1-style models with long chain-of-thought benefit most from FP4 throughput. Serve 4x more concurrent reasoning sessions per node vs H100 FP8.
192GB HBM3e per GPU keeps massive KV caches resident. Document-scale and codebase-scale agents stay in memory across turns.
Vision-language, video understanding, and speech models share HBM with text decoders on a single node — no cross-GPU activation transfers.
Forward-compatible with your H100 inference stack. Migrate when allocation lands; no architectural rewrite required.
Blackwell allocation is scarce and customers training on it are usually working with their most sensitive data — proprietary research, regulated industries, or sovereign-AI mandates. We rent single-tenant bare-metal Blackwell so the only software touching that HBM is yours.
No hypervisor, no neighbor workloads. Confidential Computing modes on Blackwell available for attested training and inference.
Full-disk encryption with keys you generate and rotate. NIST 800-88 drive sanitization between tenants. SOC 2 and HIPAA-ready deployments.
Identical HGX B200 / NVL72 builds deployable in your facility or jurisdiction — useful for EU AI Act, ITAR, FedRAMP, and sovereign-AI programs.
Dedicated VLANs, no public egress unless requested, and Direct Connect / ExpressRoute peering for training data and model weights.
We're taking reservations now for HGX B200 systems shipping in 2026. Early allocation is prioritized by term length and use case — talk to sales about your timeline.
NVIDIA quotes ~2.5x training throughput and ~15x inference throughput on FP4 for large transformer models. Real-world speedups depend heavily on whether your stack uses FP4/FP6 and the new Transformer Engine v2.
If you're shipping models in the next 6 months, rent Hopper now and migrate later — the software stacks (CUDA, NCCL, vLLM, TensorRT-LLM) port forward cleanly. If you're 12+ months out, reserving B200 allocation today is reasonable.
Slots are filling quickly. Tell us your scale target — we'll secure the right configuration.