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NVIDIA H100 vs A100: Detailed GPU Comparison for 2024

· 12 min read
Vishnu Subramanian
Vishnu Subramanian
Founder @JarvisLabs.ai

The rapid advancement of artificial intelligence and machine learning has made GPU selection more critical than ever. NVIDIA's H100 and A100 GPUs stand at the forefront of this evolution, offering unprecedented performance for complex AI workloads. In this article we explore the specifications, performance metrics, and value propositions to help you make an informed decision.

H100 vs A100 Comparison

While the H100 leads in performance, dramatic market changes have made it the clear choice for most AI workloads. H100 cloud pricing has plummeted from $8/hour to $2.85-$3.50/hour due to increased availability and provider competition. This pricing shift has effectively eliminated the A100's previous cost advantage, making the H100's superior performance (2-3x faster for most workloads) the deciding factor. Combined with the A100's limited availability and the upcoming B200 release in early 2025, organizations are increasingly standardizing on H100 for their AI infrastructure.

Table of Contents​

NVIDIA H100 vs NVIDIA A100 Specs​

The specifications reveal the H100's clear technological advantages across all major metrics. With 2.7x more CUDA cores, 3x higher FP32 performance, and 67% greater memory bandwidth, the H100 delivers substantial improvements over the A100. While the H100's higher TDP of 700W requires more robust cooling solutions, its architectural advantages and recent price reductions make it increasingly attractive for enterprise AI deployments. The improved NVLink 4.0 and PCIe Gen5 support also enable better scaling for multi-GPU configurations.

FeatureNVIDIA H100NVIDIA A100Impact
CUDA Cores18,4326,9122.7x more cores for parallel processing
Tensor Cores4th Gen (Enhanced FP8 support)3rd Gen6x faster AI training
Memory80GB HBM3 (3.35 TB/s bandwidth)80GB HBM2e (2 TB/s bandwidth)67% higher memory bandwidth
Memory TypeHBM3HBM2eFaster memory speed
Peak FP32 Perf.60 TFLOPS19.5 TFLOPS3x improvement in standard compute
ArchitectureHopperAmpereNew features like Transformer Engine
TDP700W400WRequires more robust cooling
NVLink4.0 (900 GB/s)3.0 (600 GB/s)50% faster multi-GPU scaling
PCIe SupportPCIe Gen5PCIe Gen4Higher data transfer rates
Launch Price (MSRP)~$30,000~$15,000Higher initial investment
Cloud Cost/Hour*$2.85-$3.50$1.50-$2.50More cost-effective over time
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You can launch an H100 SXM at $2.8/hour on jarvislabs.ai. No commitment, ingress/egress included.

Impact of Higher Memory Bandwidth​

The H100's 3.35 TB/s memory bandwidth (compared to A100's 2 TB/s) significantly impacts AI workloads in several ways:

Training Benefits​

  • Faster Weight Updates: Higher bandwidth allows faster reading and writing of model parameters during backpropagation
  • Larger Batch Sizes: More data can be processed simultaneously, improving training efficiency
  • Reduced Memory Bottlenecks: Less time spent waiting for data transfers between GPU memory and compute units

Inference Advantages​

  • Lower Latency: Faster data movement enables quicker model predictions
  • Higher Throughput: More concurrent inference requests can be handled
  • Better Large Model Performance: Critical for serving massive language models where weight loading speed matters

This 67% bandwidth improvement, combined with the H100's Transformer Engine, makes it particularly well-suited for large language models and vision transformers where memory access patterns are intensive.

FP8 vs FP16​

The H100's support for FP8 (8-bit floating point) precision marks a significant advancement over the A100, which lacks native FP8 capabilities. This new precision format delivers substantial benefits for AI workloads:

Performance Gains​

  • 2.2x Higher Token Generation: When using FP8 instead of FP16, especially at larger batch sizes
  • 30% Lower Latency: Reduced time-to-first-token during model inference
  • 50% Memory Savings: Enables larger batch sizes and more efficient resource utilization
  • Native Hardware Support: H100's architecture is specifically designed for FP8 operations, unlike the A100

Quality and Flexibility​

FP8 comes in two variants (E4M3 and E5M2) that balance precision and dynamic range. Despite the reduced precision, models maintain comparable quality to higher precision formats like FP16 or BF16. This makes FP8 particularly valuable for:

  • Training and serving large language models
  • Computer vision tasks
  • High-throughput AI inference workloads

For organizations running memory-intensive AI workloads, the H100's FP8 support can translate to significant cost savings through higher throughput and better resource utilization. While the A100 can use quantization techniques to approximate some FP8 benefits, it cannot match the H100's native FP8 performance.

FlashAttention-2: Supercharging Large Models​

FlashAttention-2 brings significant performance improvements to both H100 and A100, though the H100's architecture is particularly well-optimized for it. This attention mechanism optimization makes handling large language models and long sequences more efficient:

Performance Benefits​

  • 3x Faster than the original FlashAttention
  • Up to 10x Faster than standard PyTorch implementations
  • 225 TFLOPs/s achievable on A100, with even higher speeds on H100
  • Drastically Reduced Memory Usage through block-wise computation

H100-Specific Advantages​

The H100's architecture is particularly well-suited for FlashAttention-2, offering better throughput for matrix operations and improved low-latency memory access patterns. Combined with its FP8 support, this enables more efficient processing of longer sequences and larger batch sizes than the A100.

Memory Optimization​

FlashAttention-2 uses clever techniques like tiling and online softmax calculations to minimize data transfers between GPU memory and compute units. This optimization is especially powerful on H100's higher-bandwidth memory system, allowing for processing of longer sequences without running into memory bottlenecks.

Industry Benchmarks​

In a recent collaboration between Databricks and CoreWeave, the performance of NVIDIA's H100 GPUs was benchmarked against the previous generation A100 GPUs, revealing significant advancements in both speed and cost-efficiency for large language model (LLM) training.

Performance Improvements

The H100 GPUs demonstrated substantial enhancements over the A100s:

  • Training Speed: H100 GPUs achieved up to 3 times faster training times compared to A100 GPUs.

  • Cost Efficiency: With recent cloud pricing drops (H100 costs now only ~30-40% more per hour than A100), the H100's 2-3x higher throughput results in approximately 40-60% lower cost per unit of work.

Technical Enhancements

Several architectural improvements contribute to the H100's superior performance:

  • Increased FLOPS: The H100 offers 3 to 6 times more total floating-point operations per second (FLOPS) than the A100, significantly boosting computational capacity.

  • FP8 Precision Support: The introduction of FP8 data types allows for faster computations without compromising model accuracy, a feature not available in A100 GPUs.

  • Transformer Engine Integration: NVIDIA's Transformer Engine optimizes transformer-based models, enhancing performance in natural language processing tasks.

Real-World Application

In practical scenarios, these advancements enable more efficient training of large-scale models, reducing both time and cost. For instance, training a 1.3 billion parameter GPT model on H100 GPUs required no changes to hyperparameters and converged faster than on A100 GPUs.

These findings underscore the H100's capabilities in accelerating AI workloads, making it a compelling choice for organizations aiming to optimize performance and cost in large-scale AI model training.

Conclusion​

The NVIDIA H100 has emerged as the clear leader in enterprise AI acceleration for 2024. While the A100 remains a capable GPU, the H100's comprehensive advantages across performance metrics make it the superior choice for most organizations:

  • Performance Leadership: With 2.7x more CUDA cores and 3x higher FP32 performance, the H100 delivers substantially faster training and inference across all AI workloads
  • Cost-Performance Winner: Recent price reductions have eliminated the A100's cost advantage, making the H100's superior performance even more compelling
  • Future-Proof Architecture: Native FP8 support, improved memory bandwidth, and optimizations for modern AI architectures position the H100 for better long-term value
  • Market Momentum: Increasing H100 availability and the industry's shift toward H100-based infrastructure suggest stronger ecosystem support going forward

For organizations planning new AI infrastructure investments, the H100 represents the most strategic choice. Its significant performance advantages, combined with competitive pricing and architectural innovations, deliver better value across nearly all AI workloads. While the A100 served admirably as NVIDIA's flagship AI accelerator, the H100's dramatic improvements make it the definitive choice for enterprise AI in 2024.

Frequently Asked Questions (FAQ)​

Q: Is upgrading to the NVIDIA H100 worth the investment over the A100?​

A: If your workloads involve large-scale AI models requiring high memory bandwidth and faster training times, the H100 offers significant performance gains that can justify the investment. The recent price reductions for cloud instances make the H100 even more accessible.

Q: Can I use the NVIDIA H100 with my existing infrastructure designed for the A100?​

A: The H100 may require updates to your infrastructure due to its higher power consumption (700W TDP) and cooling requirements. Ensure that your servers and data centers can accommodate the increased power and thermal demands.

Q: How does the FP8 precision in the H100 benefit AI workloads?​

A: FP8 precision allows for faster computations and reduced memory usage without significantly compromising model accuracy. This leads to higher throughput, lower latency, and the ability to handle larger models or batch sizes, particularly beneficial for training and serving large language models.

Q: Is the NVIDIA A100 still a good choice in 2024?​

A: The A100 remains a capable GPU for many AI workloads. If your projects are sensitive to power consumption or budget constraints, and do not require the highest possible performance, the A100 can still be a viable option.

Q: What are the key architectural differences between the H100 and A100?​

A: The H100 is based on the newer Hopper architecture, featuring 4th Gen Tensor Cores with FP8 support, higher memory bandwidth with HBM3, and PCIe Gen5 support. The A100 is based on the Ampere architecture with 3rd Gen Tensor Cores and HBM2e memory.

Q: How do the H100 and A100 compare in terms of energy efficiency?​

A: While the H100 offers higher performance, it also consumes more power (700W TDP vs. 400W TDP for the A100). This means higher operational costs for power and cooling, which should be considered when calculating the total cost of ownership.

Q: Can the A100 emulate FP8 precision through software?​

A: The A100 does not have native FP8 support, but it can use quantization techniques to approximate some benefits of lower precision. However, it cannot match the H100's performance and efficiency with FP8 operations.

A: NVLink 4.0 provides faster interconnect bandwidth (900 GB/s) compared to NVLink 3.0 in the A100 (600 GB/s). This allows for better scaling in multi-GPU setups, reducing communication bottlenecks and improving performance in distributed workloads.

Q: How does the higher memory bandwidth of the H100 impact AI training and inference?​

A: The H100's 3.35 TB/s memory bandwidth enables faster data movement between memory and compute units. This reduces memory bottlenecks, allows for larger batch sizes, and improves overall training and inference speeds, especially in memory-intensive tasks.

Q: Are there any software or compatibility considerations when switching from A100 to H100?​

A: Most AI frameworks and software libraries support both GPUs, but to fully leverage the H100's capabilities (like FP8 precision and Transformer Engine optimizations), you may need to update your software stack to the latest versions that include these features.

Q: What are the cooling requirements for the NVIDIA H100?​

A: Due to its higher TDP of 700W, the H100 requires more robust cooling solutions, such as advanced air cooling or liquid cooling systems. Ensuring adequate cooling is essential to maintain performance and prevent thermal throttling.

Q: How does the H100's Transformer Engine enhance AI model performance?​

A: The Transformer Engine in the H100 optimizes transformer-based models by intelligently managing precision (FP8 and FP16) to accelerate training and inference while maintaining model accuracy. This results in significant performance gains for NLP and other transformer-heavy workloads.

Q: Is cloud deployment or on-premises installation better for using the H100?​

A: Cloud deployment offers flexibility and scalability without the upfront investment in hardware and infrastructure upgrades. On-premises installation provides control over your environment but requires significant capital expenditure for the GPUs and supporting infrastructure.

Q: What is the expected availability of the A100 and H100 in the market?​

A: The H100 is becoming increasingly available due to improved production and competition among providers, while the A100's availability may be limited as the industry shifts focus to the newer H100 and upcoming GPU releases.

Q: How do the H100 and A100 perform in non-AI workloads like high-performance computing (HPC)?​

A: Both GPUs are capable in HPC tasks, but the H100's advanced features and higher computational power make it better suited for demanding HPC applications that can leverage its enhanced capabilities.

Q: What should I consider when planning for future GPU upgrades beyond the H100?​

A: Stay informed about NVIDIA's roadmap and upcoming GPU releases, such as the anticipated B200 in early 2025. Consider the scalability of your infrastructure and the ease of integrating newer technologies to future-proof your investments.

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