How to Achieve Similar Performance as H100 by Using Multiple RTX 4090s

Using multiple NVIDIA RTX 4090 GPUs for AI training, including generative AI and Large Language Models, is indeed a feasible approach, but there are several factors to consider when comparing a cluster of RTX 4090 GPUs with a single H100 GPU.

1. Raw Performance and Scalability:

  • RTX 4090: It has a significant amount of power for a consumer-grade GPU, and stacking 10 of these could offer substantial raw compute capability. However, consumer GPUs like the RTX 4090 are not necessarily optimized out of the box for parallel processing across multiple units in the same way that data center GPUs are.

  • H100: Designed for scalability and high-efficiency data center operations, the H100 can handle large-scale, parallel processing tasks more efficiently. It's built to excel in environments where multiple GPUs are networked together, offering better interconnectivity and bandwidth.

2. Optimization and Software:

  • RTX 4090: To reach performance levels closer to an H100, significant optimization is needed. NVIDIA's TensorRT can optimize neural network models to improve performance and efficiency on NVIDIA GPUs. However, this requires considerable expertise and effort in tuning and optimization.

  • H100: Comes with enterprise-level software support designed for AI and machine learning tasks. This support includes optimized drivers and libraries that are geared towards maximizing performance in complex AI tasks.

3. Memory and Bandwidth:

  • RTX 4090: Even with 10 GPUs, the individual memory pools of each GPU do not aggregate. This means each GPU still only accesses its 24 GB of memory, which can be a bottleneck for very large models.

  • H100: With 80 GB of HBM3 memory and a higher memory bandwidth, it is more adept at handling larger models and datasets, which is crucial for training large language models.

4. Power Efficiency and Heat Dissipation:

  • RTX 4090: Running 10 RTX 4090 GPUs would require a significant amount of power and cooling, which might not be as efficient as running a single H100.

  • H100: Designed for high-efficiency performance in data centers, where power consumption and heat management are critical factors.

5. Cost and Accessibility:

  • RTX 4090: While individually less expensive, aggregating 10 GPUs incurs additional costs in terms of supporting infrastructure (like motherboards, power supplies, cooling systems).

  • H100: Though more expensive per unit, the H100 is designed to be cost-effective in a data center environment when considering its efficiency and the total cost of ownership.


While stacking 10 RTX 4090 GPUs and optimizing them could potentially offer comparable raw performance to a single H100 in some aspects, achieving equivalent efficiency, memory handling, and power optimization would be challenging. The H100's design for AI and machine learning at a data center scale gives it advantages in handling large-scale AI tasks that go beyond just raw compute power. For certain applications, especially those not requiring the extreme scale of large language model training, a multi-RTX 4090 setup could be a viable and cost-effective alternative.

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