8x H200 GPU — Training & Inference Deep Dive
The NVIDIA H200 SXM5 is the current flagship GPU for AI workloads, featuring 141 GB HBM3e memory at 4.8 TB/s bandwidth. An 8-GPU SXM node with NVLink 4.0/NVSwitch is the industry-standard building block for large model training and high-throughput inference.
System Snapshot
| Property |
Value |
| GPU |
NVIDIA H200 SXM5 |
| Count |
8 |
| Memory per GPU |
141 GB HBM3e |
| Total GPU Memory |
1,128 GB (1.1 TB) |
| Memory Bandwidth |
4.8 TB/s per GPU |
| FP8 Tensor Core TFLOPS |
~3,958 TFLOPS per GPU |
| BF16 Tensor Core TFLOPS |
~1,979 TFLOPS per GPU |
| GPU Interconnect |
NVLink 4.0 (900 GB/s bidirectional) |
| NVSwitch |
3rd Gen (full mesh) |
| Host Interconnect |
PCIe 5.0 / CXL |
| Form Factor |
SXM5 baseboard |
Topic Index
| # |
Topic |
Description |
| 01 |
Hardware Architecture |
Chip design, HBM3e, NVLink 4.0, NVSwitch topology |
| 02 |
Training Setup |
Distributed training, 3D parallelism, FSDP, DeepSpeed |
| 03 |
Inference Setup |
Tensor parallel inference, vLLM, TensorRT-LLM |
| 04 |
Memory Management |
KV cache, paged attention, memory pooling |
| 05 |
Performance Optimization |
Profiling, roofline, kernel tuning, CUDA Graphs |
| 06 |
Benchmarks & Validation |
MFU, MBU, latency, throughput targets |
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