The short answer: the H100 trains and serves most transformer models 2 to 3 times faster than the A100, and it rents for roughly 1.5 to 3 times the hourly price depending on the cloud. So the real decision is not which chip is faster. It is whether your workload runs hot enough to turn that speed into a lower cost per token. 

When it does, the H100 is often the cheaper option despite the higher sticker price. When it does not, the A100 still wins on cost. This guide breaks down the specs, the sourced benchmarks and the live cloud pricing, then shows the cost math that decides it. If you want the broader playbook after picking a chip, our guide to GPU cost optimization covers the levers that apply to both.

H100 vs A100 at a glance

Source for spec values: full specification comparison.

What is actually different between the H100 and A100

Both GPUs target data-center AI, but they belong to different generations and the gap is wider than the spec sheet suggests.

Architecture and the Transformer Engine

The A100 uses the Ampere generation. The H100 uses the newer Hopper generation, and its headline addition is the Transformer Engine. This unit lets the H100 run matrix math in FP8, an 8-bit precision format the A100 does not support. 

FP8 doubles the effective throughput for transformer layers without a meaningful accuracy loss on most large language models (Hopper FP8 detail). The H100 also adds a Tensor Memory Accelerator and confidential computing, neither of which the A100 offers. For teams running AI workloads built on transformers, the Transformer Engine is the single feature that justifies most of the H100 price gap.

Memory and bandwidth

The A100 ships with 40GB or 80GB of HBM2e memory and up to 2 TB/s of bandwidth. The H100 ships with 80GB of HBM3 and up to 3.35 TB/s (memory and bandwidth specs). Bandwidth matters more than raw capacity for most training and inference jobs because it sets how fast the GPU can feed its compute units. The H100 advantage of roughly 1.7x on bandwidth is a large part of why it pulls ahead on real models, not just on paper.

Interconnect and scale

When you train across many GPUs, the link between them becomes the bottleneck. The A100 uses NVLink 3.0 at 600 GB/s. The H100 uses NVLink 4.0 at 900 GB/s. At small scale the difference is modest. At large scale, where a single model is split across dozens of GPUs, the faster interconnect compounds and the H100 lead grows well beyond 3x.

Power

The A100 draws up to 400W. The H100 draws up to 700W. The H100 uses more energy per hour, but because it finishes the same job faster, energy per unit of work is usually lower. That distinction, total energy for the job rather than power at any instant, is the same logic that governs the cost comparison below.

Performance: how much faster is the H100

The H100 lead depends entirely on whether your code uses its new features. The pattern from independent benchmarks is consistent.

• On standard mixed-precision training without FP8, the H100 runs about 2.2x faster than the A100.

• With FP8 enabled through the Transformer Engine, the speedup climbs to roughly 2.7 to 3.3x, and the gap widens as model size grows (training benchmark data).

• For inference on a 70B parameter model with FP8 quantization, the H100 can reach up to 30x the throughput of an A100 in throughput-bound serving (inference benchmark).

Two facts follow from this. First, an H100 that is not running FP8 or large batches throws away most of its advantage. Second, the bigger the model and the higher the utilization, the more the H100 pulls ahead. A heavily quantized 7B model on a half-idle GPU will not show the 3x gap. A saturated 70B serving cluster will.

What the H100 and A100 cost to rent

You rarely buy these GPUs outright. Most teams rent them by the hour, so cloud pricing is the number that matters. Prices move, but the ratios are stable. For a single GPU on demand:

• A100 on demand runs around 1.99 USD per hour and H100 on demand around 2.90 to 3.29 USD per hour on mainstream clouds (on-demand cloud pricing).

• H100 on demand starts as low as 2.21 USD per hour on the cheapest specialist providers (entry H100 pricing).

• Across 15 or more providers, advertised H100 rates span roughly 1.49 to 6.98 USD per hour, a reminder that the provider you pick matters as much as the chip (H100 rental range).

On the hyperscalers the gap holds at the instance level too. An 8-GPU H100 instance runs roughly 1.5 to 3x its A100 equivalent across AWS, Google Cloud and Azure depending on the instance and term. If you want to model the full instance bill rather than the per-GPU rate, our breakdown of EC2 pricing walks through how the instance, storage and data-transfer lines add up.

Spot pricing changes the picture again. A100 spot capacity drops to around 0.45 USD per hour and H100 spot to around 0.80 USD per hour when supply allows (spot pricing data), which is where interruptible training and batch jobs find their biggest savings.

The real question: cost per unit of work

Hourly price is the wrong number to optimize. The number that decides the bill is cost per token, or more generally cost per unit of work. This is where the comparison flips, and it is the part most spec pages skip. The framing matters enough that we treat it as a core FinOps discipline rather than a hardware footnote.

Take a training job that needs 100 GPU-hours on an A100 at 1.99 USD per hour. That job costs about 199 USD. Put the same job on an H100 at 2.90 USD per hour:

• If the H100 sustains its full 3x speedup, it finishes in about 33 hours. Bill: roughly 97 USD, close to half the A100 cost.

• If the workload only reaches a 1.3x speedup, because it cannot use FP8 or runs small batches, it takes about 77 hours. Bill: roughly 223 USD. The A100 now wins.

The break-even point sits exactly at the hourly price ratio, which here is about 1.5x (2.90 divided by 1.99). Whenever the H100 real speedup beats its price premium, it is cheaper per unit of work. Below that line the A100 is cheaper. 

So the decision reduces to one honest question about your own code: can it actually saturate an H100. The practitioner habit worth copying is simple. Run your real workload for 30 minutes on each GPU at on-demand rates, then compute cost per million tokens at your production batch size. 

The measured number settles the argument better than any spec table, and our notes on cost versus performance cover how to frame that tradeoff for stakeholders.

How to cut the effective cost of either GPU

Whichever chip you land on, the on-demand rate is the worst price you will ever pay. Three levers bring it down.

• Commitments: Reserved capacity and savings plans cut steady-state GPU costs sharply in exchange for a one or three year term. For always-on inference this is usually the largest single saving. Our comparison of savings plans and reserved instances shows how to choose between them.

• Spot capacity: Interruptible spot instances cut the rate by 60 to 80 percent and suit training runs and batch jobs that checkpoint and resume. The tradeoff is eviction risk, covered in our guide to spot instances.

• Utilization: A GPU at 30 percent utilization wastes most of what you pay for. Rightsizing, batching and scheduling lift utilization so each dollar buys more work. This is also where the H100 case strengthens, since a fuller GPU pushes you past the break-even point. Purpose-built FinOps tools for AI cost management surface idle GPU spend that billing dashboards hide.

Stacking commitments on the steady base, spot on the burst, and high utilization on both routinely cuts effective GPU cost by half or more against naive on-demand.

Which GPU should you choose

Match the chip to the workload rather than to the spec sheet.

Choose the H100 when:

• You pre-train or fine-tune large models, especially 30B parameters and above.

• Your serving stack uses FP8 and runs at high, sustained utilization.

• You need the lowest possible latency for real-time inference on large models.

• Your job can saturate the GPU, pushing its real speedup past the hourly price ratio.

Choose the A100 when:

• You run moderate-size or heavily quantized models where the H100 advantage stays small.

• Your workloads are bursty or under-utilized, so the cheaper hourly rate wins.

• You run batch or background inference where throughput matters more than latency.

• Availability or budget rules out H100 capacity, which remains harder to secure.

Conclusion

The H100 is the faster GPU and, for large saturated transformer workloads, often the cheaper one per unit of work despite a price that runs up to 3x the A100. The A100 stays the better value for moderate models, bursty demand and budget-bound teams. The decision is a utilization question, not a horsepower question. 

Measure cost per token on your own workload, layer commitments and spot on top of the right chip, and keep utilization high. That sequence, not the chip alone, is what controls the bill.

Frequently Asked Questions

Is the H100 worth 3x the price of the A100?

Yes when your workload saturates it. Above roughly a 1.8x real speedup the H100 costs less per token despite the higher hourly rate. For small, quantized or under-utilized models the A100 stays cheaper.

How much faster is the H100 than the A100?

About 2.2x faster on standard mixed-precision training, rising to 2.7 to 3.3x with FP8 enabled, and the gap grows with model size. Inference gains can reach far higher on large quantized models.

Is the A100 still a good GPU for AI?

Yes. The A100 remains cost-effective for moderate-size models, batch inference and bursty workloads, and its lower hourly and spot pricing often beats the H100 on cost per token when utilization is low.

What is the main difference between the H100 and A100?

The H100 adds the Hopper Transformer Engine with FP8 support, 80GB of faster HBM3 memory and a 900 GB/s NVLink. These give it 2 to 3x the throughput on transformer models the A100 cannot match.

Should I use H100 or A100 for LLM inference?

Use the H100 for low-latency serving of large models at high traffic, where FP8 and big batches shine. Use the A100 for smaller models or moderate traffic where its lower cost per token wins.

How can I lower H100 and A100 cloud costs?

Commit to reserved capacity or savings plans for steady workloads, use spot instances for interruptible jobs, and raise utilization through rightsizing and batching. Together these cut effective GPU cost by half or more.