HGX H200 brings Hopper compute together with 141GB of HBM3e per GPU and 4.8 TB/s memory bandwidth. The right rental when KV cache, context length, or batch size — not compute — is your bottleneck.
8 × 141GB HBM3e — fit a 405B Llama 3.1 in FP8 on a single server with room for a real KV cache.
Memory bandwidth jumps 43% vs H100. Token-generation throughput on long context scales nearly linearly.
Same 3,958 TFLOPS FP8 sparse as H100 — your training and inference code runs unchanged.
32K, 128K, 1M-token contexts where KV cache size dominates. H200 doubles serveable concurrency vs H100 at the same latency.
Mixture-of-experts models (Mixtral, DeepSeek, Llama 4) benefit directly from more HBM and bandwidth — fewer cards, simpler topology.
CUDA, cuDNN, NCCL, vLLM, TensorRT-LLM — no changes. Just more memory and more bandwidth.
Modern serving is memory-bound, not compute-bound. Once context windows pass 32K tokens, KV cache — not matmul — decides how many concurrent users a node can serve. H200's 141GB of HBM3e per GPU and 4.8 TB/s bandwidth let you keep more sessions resident and feed the tensor cores without stalling.
Llama 3.1 405B at 128K, Claude-style document QA, and full-codebase agents stay resident in HBM. No paging to host memory, no tail-latency cliff.
Drop-in with H100 stacks. PagedAttention, continuous batching, and FP8 KV cache compression all benefit directly from the extra memory headroom.
Mixture-of-experts weights fit in fewer cards. Fewer GPUs per replica means simpler topology, lower NCCL overhead, and better $/token in production.
43% more memory bandwidth than H100 translates almost linearly to token-generation throughput at the same latency SLA.
H200 keeps Hopper's 3,958 TFLOPS FP8 sparse compute and adds the memory you need for large-batch SFT, RLHF, and embedding pipelines. Code that ran on H100 runs unchanged — you just stop hitting OOMs.
Larger effective batch sizes without gradient accumulation. Reward-model passes and PPO rollouts share HBM with the policy model on a single node.
Bulk-embed corpora 1.4–2x faster than H100. Pair with NVMe scratch for end-to-end vector index builds without spilling to host RAM.
Fit Llama 3.1 405B in FP8 on an HGX H200 with real KV cache headroom — no cross-node tensor parallelism required for inference.
Fine-tune by day, serve by night on the same hardware. No re-quantization surprises, no migration between SKUs.
Long-context inference often means sensitive data — full medical records, internal codebases, legal discovery, customer chat history. H200 nodes ship as bare metal so the only software with access to that HBM is yours.
No hypervisor, no shared GPU, no neighbor tenant. Confidential Computing on Hopper available for attested workloads.
Customer-held FDE keys, NIST 800-88 drive sanitization between tenants, and BAAs available for healthcare deployments.
Identical H200 builds deployable in your facility or a dedicated cage — useful for ITAR, FedRAMP-track, or contract-isolated inference.
No public egress unless you ask for it. Direct Connect / ExpressRoute peering keeps prompts and completions off the open internet.
Same Hopper GPU compute, but with 141GB of HBM3e per GPU (vs 80GB HBM3 on H100) and 4.8 TB/s memory bandwidth (vs 3.35 TB/s). For memory-bound workloads — long-context inference, larger batch sizes, KV cache — H200 delivers a real 1.4–2x speedup over H100 with no code changes.
We have HGX H200 allocations landing on a rolling basis. Typical lead time today is 6–10 weeks. Reserve a slot early — H200 supply is tighter than H100 was at launch.
For inference and fine-tuning of long-context models (32K+ tokens) and large MoE architectures, yes. For pure compute-bound pretraining of dense models under 32K context, H100 SXM5 still wins on price/performance.
Reserve early — we'll hold a slot while you finalize specs.