The A100 is a proven, cost-efficient GPU for training and inference, while the H100 delivers significantly higher throughput for modern AI workloads. This comparison analyzes performance, architecture, and cost.
Takeaways
- H100 delivers higher performance, especially for LLM training and inference:
- Faster memory bandwidth.
- Transformer Engine optimizes it for transformer-based models.
- NVLink 4.0 for improved multi-GPU configurations.
- A100 remains a cost-efficient option for production workloads.
- Thunder Compute simplifies benchmarking both GPUs.
Quick Comparison Table
This is a snapshot of NVIDIA A100 vs H100 specs and capabilities.
| Feature | NVIDIA A100 | NVIDIA H100 |
|---|---|---|
| Architecture | Ampere | Hopper |
| Process Node | 7nm | 4nm |
| CUDA Cores | 6,912 | 14,592 |
| Tensor Cores | 3rd Gen | 4th Gen |
| Memory | 80GB HBM2e | 80GB HBM2e or HBM3 |
| Memory Interface | 5120-bit | 5120-bit |
| Memory Bandwidth | 1,935 GB/s | 2,040 GB/s PCIe - 3,000 GB/s SXM |
| FP16 Tensor Performance | 312 TFLOPS | 989 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 51.2 TFLOPS |
| Transformer Engine | No | Yes |
| NVLink | 3.0 (600GB/s) | 4.0 (600-900GB/s) |
| System Interface | PCIe 4.0 x16 | PCIe 5.0 x16 |
| Power (TBP) | 300W | 350-700W |
| Best For | Cost-efficient training/inference | Cutting-edge AI + LLM workloads |
| Cloud Provider Prices | $0.78-$5.07/hr | $1.38-$11.06/hr |
| Purchase Price (new, 80GB) | $9,500–$14,000 | $22,000–$40,000 |
Beyond the difference in raw speed, the economic divide is significant; the H100 has higher rental costs, but is more efficient for large-scale projects as it "delivers approximately 40-60% lower cost per unit of work" compared to the A100.
A100 vs H100 Specs
The specifications below are based primarily on NVIDIA’s official datasheets, which are the most reliable source for raw hardware data.
Architecture: Ampere vs. Hopper
The A100 uses Ampere architecture; the standard for large-scale AI training over the past few years. It introduced powerful tensor cores and strong mixed-precision performance that made it a staple across cloud providers. Even in 2026, it remains widely deployed and well-supported.
The H100 uses newer Hopper architecture in AI workloads. Its Transformer Engine adjusts precision to accelerate deep learning models. This translates into a 2.15× throughput edge of the H100 over the A100 in training tasks.
Memory Bandwidth
Memory bandwidth can be an important difference between the two GPUs:
- The A100 delivers 2 TB/s using HBM2e memory.
- The H100 varies depending on form factor:
- PCIe delivers 2 TB/s with HBM2e memory.
- SXM amps this up to 3.35 TB/s using HBM3.
The H100 is pretty much the same as the A100 in its PCIe variant, but is over 50% faster in SXM form factor. As models grow larger and more memory-bound, this advantage carries more impact in real-world performance.
Latency and Concurrency
The H100 introduces improvements in how workloads are scheduled and executed. These changes allow better concurrency, meaning more tasks can run efficiently at the same time. This is particularly valuable in shared or multi-tenant GPU environments.
Lower latency is another benefit, especially for inference workloads that require quick response times. In practice, this makes the H100 better suited for production systems serving real-time AI applications.
PCIe vs SXM
Both A100 and H100 are available in PCIe and SXM configurations, which affects how they are deployed. PCIe versions are more flexible and easier to integrate into standard servers, making them common in cloud environments. SXM variants, on the other hand, are designed for high-performance systems.
SXM configurations benefit from faster interconnects and improved thermal performance. This is especially important for H100, where NVLink 4.0 enables significantly better multi-GPU communication.
Cost of Ownership
Hourly rates only tell part of the story. The rest of the story is told by the unit of work completed.
The H100 has higher rental costs, but is more efficient for large-scale projects. Its higher upfront cost is offset by better total cost of ownership, with 2–3× throughput in AI workloads translating to approximately 40–60% lower cost per unit of work for large-scale tasks.
Purchase Price
For on-premise deployments initial investment is substantial. A single A100 80GB goes for $9,500-$14,000 new, depending on vendor and condition, while an 8-GPU DGX A100 server can exceed $150,000 excluding power and maintenance.
The H100 is more expensive and has a wider pricing spread. The PCIe 80GB model typically costs $22,000-$30,000 depending on vendor and configuration, while SXM variants optimized for dense server deployments range from $35,000-$40,000. An 8-GPU DGX H100 server system costs over $400,000.
Cloud Rental
Cloud pricing varies widely. Since May 2025, A100 on-demand pricing has decreased by about 15% dropping from $2.39 to $2.02/hr per GPU. At the same time, H100 on-demand pricing increased by about 13% from $3.35 to $14.90/hr per GPU, with the range running from $1.38/hr to $11.06/hr depending on provider and instance type.
Provider choice matters enormously. The same H100 costs $1.38/hr on Thunder Compute but $11.06/hr on Google Cloud.
Buy vs. Rent
The break-even point depends on utilization. At $1.20/hr, 1 year of continuous usage costs roughly $10,500. Cloud clearly wins for under 600 GPU-hours/month; purchase wins above that threshold.
Performance
Large Language Model (LLM) Training
When evaluating A100 vs H100 performance for LLM training, the difference is substantial. The H100 can deliver several times faster training speeds depending on the model size and optimization strategy. This is largely due to its Transformer Engine and support for lower precision formats like FP8.
For very large models, the efficiency gains compound over time, reducing overall training cost despite higher hourly pricing. As a result, the H100 has become the preferred choice for cutting-edge AI development.
Benchmark: NVIDIA A100 vs. H100 (4-GPU Configuration)
| Model | Application | A100 Latency (min) | H100 Latency (min) | Speedup (H100 vs A100) |
|---|---|---|---|---|
| RetinaNet | Object Detection | 176.84 | 107.46 | 1.65x |
| ResNet-50 | Image Classification | 61.28 | 39.92 | 1.54x |
| 3D U-Net | Medical Imaging | 48.05 | 32.00 | 1.50x |
| Mask R-CNN | Object Detection | 81.86 | 55.18 | 1.48x |
| RNN-T | Speech Recognition | 64.05 | 45.92 | 1.39x |
| Source: MLPerf Training v3.0 (Dell) | ||||
The benchmark compares systems with similar hardware:
- 80GB of High Bandwidth Memory.
- PCIe form factor.
- Nodes with 4 GPUs.
Inference Throughput
Inference is another area where the H100 stands out. The table below compares real-world token throughput across model sizes and configurations.
| Model / Config | Metric | A100 | H100 | H100 Advantage |
|---|---|---|---|---|
| 13B–70B range (typical deployment) | Tokens/sec | ~130 | 250–300 | ~2× |
| Llama 3.1 70B, batch 64, vLLM | Tokens/sec | 1,148 | 3,311 | 2.8× |
| Llama 3.1 70B, batch 64, vLLM | Hourly cost multiplier | 1× | 1.7× | 2.8× throughput at 1.7× cost |
| Models under 30B (<1,000 tok/s target) | Best option | A100 (cost leader on spot) | Overkill for this range | — |
| Source: OpenMetal (2025) | ||||
For high-traffic applications such as chatbots and real-time AI services, the H100's throughput advantage makes it the more cost-efficient choice at scale. The A100 remains a strong option for smaller models or latency-tolerant workloads.
Scaling with NVLink and NVSwitch
Both GPUs support scaling across multiple nodes, but the H100 improves on this. With NVLink 4.0, it offers higher bandwidth between GPUs, which reduces bottlenecks in distributed training. This is particularly important for large-scale AI systems.
The improved interconnect performance also enhances efficiency when scaling across clusters. In practice, this means fewer resources are wasted on communication overhead, leading to better overall utilization.
Quantifying the Ampere Legacy
The H100 has taken over for most intensive modern workloads, but the A100’s track record remains visible in the sheer volume of research it has powered. As shown below, the NVIDIA A100 has been used to train a "total of 84 notable AI models," making it the most prolific accelerator in history.
* AI Index Repost: 1.2 Compute and Infrastructure
Use Cases
When to Choose the A100
The A100 remains a strong choice for many organizations for its cost efficiency. It provides reliable performance for a wide range of AI workloads, including training and inference. Its maturity also means better ecosystem support and stability.
For teams running moderate-scale models or optimizing cloud spend, the A100 continues to deliver excellent value. It is often the default option for production systems that do not require cutting-edge performance.
When to Choose the H100
The H100 is best suited for teams pushing the limits of modern AI. Newer GPUs exist but they are not widely accesible. Meanwhile, the H100 still excels in large-scale model training, high-throughput inference, and distributed workloads. Its advanced features make it particularly effective for transformer-based architectures.
While it comes at a higher cost, the performance gains can justify the investment for many use cases.
Final Thoughts on NVIDIA A100 vs H100
Both GPUs have a place in modern infrastructure, and the comparison ultimately comes down to your specific workload and priorities.
- The A100 is more cost-effective and has proven reliability.
- The H100 delivers significantly higher performance.
Thunder Compute offers both A100 and H100 GPUs at highly competitive prices, making it easy to compare them side by side. With on-demand access, you can quickly deploy, benchmark, and scale based on your needs.
To match the right hardware to your workload, see our GPU selection guide for AI workflows
FAQ
Is the NVIDIA H100 Better Than the A100?
In most cases, yes. The H100 delivers significantly higher performance, particularly for AI training and inference involving large models. However, the A100 is still highly capable and often more cost-efficient. The best choice depends on your budget and the scale of your workload.
Where Can I Rent H100 Or A100 GPUs Instantly?
You can rent both GPUs through cloud providers that offer GPU-as-a-service platforms. Thunder Compute provides instant access to A100 and H100 instances at market low prices of $0.78 and $1.38 per hour. Billed by the minute, no commitments, no egress fees.
What is H100 and A100 Cloud Rental Monthly Cost in 2026
Pricing varies depending on provider, region, and usage patterns. Running the A100 for a month straight can cost anywhere from $524 on Thunder Compute, to $3701 on Google Cloud. For the H100 the gap is even larger, costing $927 on Thunder Compute and $8074 on Google Cloud.