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'''NVIDIA H100 Server''' is a professional AI/ML GPU cloud server available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud]. The H100 is NVIDIA's workhorse data center GPU, widely adopted for AI training and inference across the industry.

== Specifications ==
{| class="wikitable"
|-
! Component !! Specification
|-
| '''GPU''' || NVIDIA H100 SXM (Hopper architecture)
|-
| '''VRAM''' || 80 GB HBM2e
|-
| '''Memory Bandwidth''' || 3.35 TB/s
|-
| '''FP16 Performance''' || ~989 TFLOPS
|-
| '''FP8 Performance''' || ~1,979 TFLOPS
|-
| '''Interconnect''' || NVLink 4.0 (900 GB/s)
|-
| '''Starting Price''' || From $3.83/hr
|}

== Performance ==
The H100 is the industry standard for AI/ML workloads in 2024–2026. Key performance characteristics:
* '''4th-gen Tensor Cores''' with FP8 support — 2x throughput vs A100 for training
* '''3.35 TB/s memory bandwidth''' — 2x the A100's bandwidth
* '''Transformer Engine''' — hardware acceleration specifically for transformer-based models
* '''80 GB HBM2e''' — sufficient for most production models

Compared to the [[NVIDIA A100 Server]] ($2.37/hr):
* 2–3x faster for transformer training (FP8 + Transformer Engine)
* 2x higher memory bandwidth
* Same VRAM capacity (80 GB)
* 62% higher cost per hour, but 40–60% less total cost for training jobs due to speed

== Best Use Cases ==
* AI model training (7B–70B parameter models)
* Large-scale inference serving
* Fine-tuning foundation models (LoRA, QLoRA, full fine-tune)
* Natural language processing research
* Computer vision model training
* Generative AI (text, image, video generation)
* Reinforcement learning from human feedback (RLHF)

== Pros and Cons ==
=== Advantages ===
* Industry-standard AI training GPU
* FP8 Tensor Cores for maximum training throughput
* Transformer Engine for transformer model acceleration
* 80 GB VRAM handles most production models
* Excellent software ecosystem (CUDA, cuDNN, TensorRT)
* NVLink 4.0 for efficient multi-GPU training

=== Limitations ===
* 80 GB VRAM may be tight for 70B+ models without quantization
* $3.83/hr cost accumulates quickly for long training runs
* High demand can affect availability
* Requires CUDA expertise for optimal utilization

== Pricing ==
Available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud] starting at '''$3.83/hr'''. For context: training a 7B model fine-tune might take 4–8 hours ($15–30), while training from scratch can cost hundreds to thousands of dollars.

== Recommendation ==
The '''NVIDIA H100 Server''' is the default recommendation for serious AI/ML workloads. It offers the best balance of performance, VRAM capacity, and cost for most use cases. Start here if you're training or fine-tuning models in the 7B–70B range. For budget-conscious workloads, consider the [[NVIDIA A100 Server]]. For maximum VRAM, upgrade to the [[NVIDIA H200 Server]].

== See Also ==
* [[NVIDIA H200 Server]]
* [[NVIDIA H100 NVL Server]]
* [[NVIDIA A100 Server]]
* [[NVIDIA RTX 4090 Server]]

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

NVIDIA H100 Server - Revision history

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