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The GPU That Doubles H100 Memory Without Changing the Rest of Your Cluster
The H100 has an 80 GB HBM3 ceiling. The NVIDIA H200 removes it — 141 GB of HBM3e with 4.8 TB/s memory bandwidth, а 75 percent capacity increase and 60 percent bandwidth increase over H100, in the same SXM5 form factor with the same NVLink topology. For LLM teams whose models exceed 80 GB per GPU — 70B parameter models in full precision, MoE models with large expert weights, or long-context inference with huge KV caches — the H200 is the drop-in upgrade that doubles usable memory per GPU without redesigning the cluster fabric.
Технические характеристики
| Параметр | Спецификация |
|---|---|
| Архитектура графического процессора | NVIDIA Hopper (GH100, same die as H100 SXM5) |
| Цвета CUDA | 16,896 |
| Тензорные ядра | 528 (4th Gen, Hopper) |
| Память | 141 GB HBM3e |
| Пропускная способность памяти | 4.8 ТБ/с (vs H100’s 3.35 ТБ/с, +43 процент) |
| Memory Capacity | 141 ГБ (vs H100’s 80 ГБ, +76 процент) |
| Интерфейс | PCIe 5.0 х16 (SXM5 module via NVSwitch or PCIe variant) |
| НВЛинк | 900 ГБ/с (НВСвитч, вплоть до 8 GPUs in one node) |
| FP8 Tensor Core (с редкостью) | 3,958 терафлопс |
| FP16/BF16 Tensor Core (с редкостью) | 1,979 терафлопс |
| Тензорное ядро TF32 | 989 терафлопс |
| ФП64 | 67 терафлопс |
| Трансформаторный двигатель | Да (РП8, automatic precision selection) |
| МНЕ (Многоэкземплярный графический процессор) | Вплоть до 7 instances |
| TDP | 700Вт (SXM5), ~600W (вариант PCIe) |
| Фактор формы | SXM5 module (8-GPU NVLink node) или PCIe 5.0 двухслотовый |
| Supported Platforms | Dell XE9680 (8x H200), xFusion G5500 V7 (up to 8-10x), NVIDIA DGX H200 |
| Гарантия | 3-год (с сервером) |
H200 vs H100 vs A100 — The Memory Ceiling Comparison
| Особенность | H200 141GB | H100 80 ГБ | А100 80 ГБ |
|---|---|---|---|
| Память | 141 GB HBM3e | 80 ГБ HBM3 | 80 ГБ HBM2e |
| Пропускная способность памяти | 4.8 ТБ/с | 3.35 ТБ/с | 2.0 ТБ/с |
| FP8 Tensor (sparsity) | 3,958 терафлопс | 3,958 терафлопс (same compute die) | Н/Д (no FP8) |
| 70B Model Fit (FP16, no quant) | Да (141 GB holds ~140 GB weights+KV) | Нет (needs 2 GPUs or quantization) | Нет (needs quantization) |
| Long-Context Inference (100K+ tokens) | Отличный (large KV cache fits) | KV cache overflows to CPU | KV cache overflows to CPU |
| Цена (относительно H100) | ~135-145 percent | 100 процент | ~50 percent |
Why the 141 GB Matters: The Model Fitting Problem
Training or serving a 70B parameter model in BF16 requires roughly 140 GB of GPU memory — 70B x 2 bytes per parameter. The H100’s 80 GB cannot hold it: you either quantize (losing precision), use tensor parallelism across 2 графические процессоры (doubling memory traffic and halving scaling efficiency), or stream weights (killing throughput). The H200’s 141 GB fits the full model plus KV cache on a single GPU. For inference, a 32K-token context window with a 70B model consumes ~20 GB of KV cache on top of weights — the H200 is the only single-GPU option that fits both without spilling. This is the difference between an inference server that serves one request at a time and one that serves 8-16 concurrent requests.
Совместимые платформы
SXM5 variant: Dell XE9680 (8x H200, NVLink NVSwitch fabric), xFusion G5500 V7, NVIDIA DGX H200. вариант PCIe: any server with 700W GPU power delivery and adequate airflow. Verify server firmware supports H200 before ordering — H200 requires BIOS/BMC updates on platforms originally shipping with H100.
Источник через Синькуань
We configure H200 GPU servers with factory-direct pricing, 3-год гарантии, и доставка по всему миру. Lead times are shorter than 2025 H200 allocation; contact us for current availability.
