Xincuan 서버 | 엔터프라이즈 서버 하드웨어 공급업체
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, ㅏ 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.
기술 사양
| 매개변수 | 사양 |
|---|---|
| GPU 아키텍처 | NVIDIA Hopper (GH100, same die as H100 SXM5) |
| 쿠다 색상 | 16,896 |
| 텐서 코어 | 528 (4세대, Hopper) |
| 메모리 | 141 GB HBM3e |
| 메모리 대역폭 | 4.8 TB/초 (vs H100’s 3.35 TB/초, +43 퍼센트) |
| Memory Capacity | 141 GB (vs H100’s 80 GB, +76 퍼센트) |
| 인터페이스 | PCIe 5.0 x16 (SXM5 module via NVSwitch or PCIe variant) |
| NV링크 | 900 GB/초 (NV스위치, 까지 8 GPUs in one node) |
| FP8 Tensor Core (희박하게) | 3,958 테플롭스 |
| FP16/BF16 Tensor Core (희박하게) | 1,979 테플롭스 |
| TF32 텐서 코어 | 989 테플롭스 |
| FP64 | 67 테플롭스 |
| 변압기 엔진 | 예 (FP8, automatic precision selection) |
| 나 (멀티 인스턴스 GPU) | 까지 7 instances |
| TDP | 700승 (SXM5), ~600W (PCIe 변형) |
| 폼 팩터 | SXM5 module (8-GPU NVLink node) 또는 PCIe 5.0 듀얼 슬롯 |
| Supported Platforms | Dell XE9680 (8x H200), xFusion G5500 V7 (up to 8-10x), NVIDIA DGX H200 |
| 보증 | 3-년도 (서버와 함께) |
H200 vs H100 vs A100 — The Memory Ceiling Comparison
| 특징 | H200 141GB | H100 80GB | A100 80GB |
|---|---|---|---|
| 메모리 | 141 GB HBM3e | 80 GB HBM3 | 80 GB HBM2e |
| 메모리 대역폭 | 4.8 TB/초 | 3.35 TB/초 | 2.0 TB/초 |
| FP8 Tensor (sparsity) | 3,958 테플롭스 | 3,958 테플롭스 (same compute die) | 해당 없음 (no FP8) |
| 70B Model Fit (FP16, no quant) | 예 (141 GB holds ~140 GB weights+KV) | 아니요 (needs 2 GPUs or quantization) | 아니요 (needs quantization) |
| Long-Context Inference (100K+ tokens) | 훌륭한 (large KV cache fits) | KV cache overflows to CPU | KV cache overflows to CPU |
| 가격 (H100 대비) | ~135-145 percent | 100 퍼센트 | ~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 GPU (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.
호환 플랫폼
SXM5 variant: Dell XE9680 (8x H200, NVLink NVSwitch fabric), xFusion G5500 V7, NVIDIA DGX H200. PCIe 변형: 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.
Xicuan을 통한 소스
We configure H200 GPU servers with factory-direct pricing, 3-1년 보증, 그리고 글로벌 배송. Lead times are shorter than 2025 H200 allocation; contact us for current availability.
