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'''NVIDIA H200 Server''' is a flagship GPU cloud server available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud]. The H200 is NVIDIA's most powerful data center GPU, featuring 141 GB of HBM3e memory and massive compute throughput for large-scale AI training and inference.

== Specifications ==
{| class="wikitable"
|-
! Component !! Specification
|-
| '''GPU''' || NVIDIA H200 (Hopper architecture)
|-
| '''VRAM''' || 141 GB HBM3e
|-
| '''Memory Bandwidth''' || 4.8 TB/s
|-
| '''FP16 Performance''' || ~989 TFLOPS
|-
| '''FP8 Performance''' || ~1,979 TFLOPS
|-
| '''Interconnect''' || NVLink 4.0 (900 GB/s)
|-
| '''Starting Price''' || From $4.74/hr
|}

== Performance ==
The NVIDIA H200 is the successor to the H100, with the same Hopper architecture but significantly upgraded memory:
* '''141 GB HBM3e''' vs 80 GB HBM2e on the H100 — 76% more VRAM
* '''4.8 TB/s memory bandwidth''' vs 3.35 TB/s on H100 — 43% more bandwidth
* Identical compute units but memory improvements accelerate memory-bound workloads by 40–90%

For LLM training, the extra VRAM means larger models can fit on a single GPU without model parallelism overhead. For inference, you can run larger batch sizes or serve bigger models without splitting across multiple GPUs.

Compared to the [[NVIDIA H100 Server]] ($3.83/hr), the H200 costs approximately 24% more per hour but delivers substantially better performance for memory-bound workloads, making it more cost-effective per token for large model inference.

== Best Use Cases ==
* Training large language models (70B+ parameters)
* Fine-tuning foundation models (LLaMA, Mistral, GPT)
* Large-scale inference serving for production AI
* Scientific simulations with large memory requirements
* Multi-modal model training (vision + language)
* Research requiring state-of-the-art GPU hardware

== Pros and Cons ==
=== Advantages ===
* 141 GB HBM3e — largest GPU memory available
* 4.8 TB/s memory bandwidth eliminates memory bottlenecks
* Hopper architecture with FP8 tensor cores
* NVLink 4.0 for efficient multi-GPU scaling
* 40–90% faster than H100 on memory-bound workloads

=== Limitations ===
* Highest per-hour cost at $4.74/hr
* Overkill for small models or inference-only workloads
* Limited availability due to high demand
* Requires expertise to fully utilize the hardware
* Cost adds up for sustained training runs

== Pricing ==
Available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud] starting at '''$4.74/hr'''. Multi-GPU configurations available for distributed training. Monthly costs depend on usage patterns — a dedicated 24/7 instance runs approximately $3,413/month.

== Recommendation ==
The '''NVIDIA H200 Server''' is the right choice when you need the absolute fastest GPU available and your workload is memory-bandwidth bound. If you're training models with 70B+ parameters, running large-batch inference, or doing cutting-edge AI research, the H200's 141 GB HBM3e provides a clear advantage. For workloads that fit within 80 GB VRAM, the [[NVIDIA H100 Server]] offers similar compute at lower cost.

== See Also ==
* [[NVIDIA H100 Server]]
* [[NVIDIA H100 NVL Server]]
* [[NVIDIA A100 Server]]

[[Category:GPU Servers]]
[[Category:AI Training]]
[[Category:Data Center GPU]]

NVIDIA H200 Server - Revision history

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