Quick verdict: The H100 is the better choice for most transformer training and inference, typically finishing jobs two to three times faster and often at a lower total cost than the A100 despite a higher hourly rate. The A100 still makes sense for smaller models, classical machine learning, and budget-sensitive batch work. Choose the H200 only when memory is the bottleneck. Compare cost per completed job, not cost per hour.
NVIDIA A100 GPU: Overview
The A100 launched in 2020, powered by NVIDIA’s Ampere architecture. It made large-scale AI training more practical and became a popular data center accelerator for AI training, inference, data analytics, and scientific computing. It features 54.2 billion transistors, 6,912 CUDA cores in its SXM form, and 40 GB or 80 GB of HBM2e memory.
NVIDIA H100 GPU: Overview
The H100 launched in 2022, based on NVIDIA’s Hopper architecture. Unlike the A100, which serves a broad range of workloads, the H100 was designed to accelerate transformer models, including the large language models used in modern AI applications. Built on a 4nm process, it features 80 billion transistors, 16,896 CUDA cores in its SXM form, fourth-generation Tensor Cores, and 80 GB of faster HBM3 memory. It also introduces specialized features that improve performance for demanding AI workloads.
Architecture: Ampere vs Hopper
The A100’s third-generation Tensor Cores extended mixed-precision training, which Tensor Cores first made practical on the V100, by adding BF16 and TF32 support. In this approach, a model keeps a master copy of its weights in 32-bit precision for numerical stability while running the heavy forward and backward passes in 16-bit formats such as BF16 or FP16. This delivers up to 312 TFLOPS of dense Tensor Core performance, many times faster than plain FP32.
The A100 also introduced Multi-Instance GPU (MIG), which divides a single card into as many as seven fully isolated instances, each with its own memory and compute. That feature made the A100 attractive for shared and multi-tenant environments.
The H100 builds on this in several ways. Its most significant addition is native support for FP8, an 8-bit floating-point format that halves the memory footprint compared with 16-bit precision and can roughly double throughput for operations that tolerate the lower precision. Because 8 bits leave very little numerical range, NVIDIA created two FP8 variants, one that favors precision for weights and activations and one that favors range for gradients.
Managing this by hand would be tedious and error-prone, so the H100 includes the Transformer Engine, which analyzes each layer of a model, runs matrix operations in FP8 where it is safe, and moves to higher precision for sensitive operations such as normalization and softmax. It also tracks and adjusts the scaling factors that FP8 requires. For developers, the practical result is that FP8 speed usually needs only modest code changes, typically by using NVIDIA’s Transformer Engine library or a supported framework integration, and models trained this way can run inference in FP8 directly.
The H100 also adds second-generation MIG, confidential computing, the Tensor Memory Accelerator that moves data without tying up compute threads, and DPX instructions that speed up dynamic programming algorithms. Confidential computing deserves particular attention from regulated industries. Alongside encryption at rest and in transit, it protects data while it is actively being processed, which can be a deciding factor for some regulated healthcare and financial workloads, provided your hosting provider enables it. If compliance matters, look for providers offering isolated dedicated servers with confidential computing support.
Specifications Compared
The figures below are for the SXM versions of each GPU, which is what major cloud providers deploy in multi-GPU servers. NVIDIA’s headline peak numbers include structured sparsity, so the table shows both sparse and dense figures. Dense performance is half the sparse number.
| Specification | A100 80GB SXM | H100 SXM |
|---|---|---|
| Architecture | Ampere | Hopper |
| Process | 7nm | 4nm |
| Transistors | 54.2 billion | 80 billion |
| CUDA cores | 6,912 | 16,896 |
| Tensor Cores | 432 (3rd gen) | 528 (4th gen) |
| Memory | 80 GB HBM2e | 80 GB HBM3 |
| Memory bandwidth | 2.0 TB/s | 3.35 TB/s |
| L2 cache | 40 MB | 50 MB |
| FP64 (non-Tensor) | 9.7 TFLOPS | 34 TFLOPS |
| TF32 Tensor, dense | 156 TFLOPS | 494 TFLOPS |
| BF16/FP16 Tensor, dense | 312 TFLOPS | 989 TFLOPS |
| BF16/FP16 Tensor, with sparsity | 624 TFLOPS | 1,979 TFLOPS |
| FP8 Tensor, dense | Not supported | 1,979 TFLOPS |
| FP8 Tensor, with sparsity | Not supported | 3,958 TFLOPS |
| NVLink | 600 GB/s | 900 GB/s |
The headline takeaway is that the H100 delivers roughly three times the raw compute of the A100 at every shared precision level, adds an entire precision tier the A100 cannot touch, and moves data about 67 percent faster through its memory. One point that often surprises buyers is that memory capacity is the same at 80 GB. The H100 does not let you fit a larger model on a single card than the 80 GB A100 does. What it does is move that memory far faster, and that speed matters enormously for inference.
Real-World Performance: What the Benchmarks Show
Specifications describe potential, while benchmarks show what happens under real workloads, and the two do not always agree. NVIDIA’s own figures claim gains from about 1.5x up to 4x or more for GPT-3-class training, and higher still for large clusters that use the NVLink Switch System. These numbers come from the company selling the hardware and typically reflect heavily tuned software and FP8 precision, so they are best read as a ceiling.
Independent results give a more realistic picture. MosaicML’s tests on CoreWeave cloud instances measured speedups of roughly 2.2x to 3.3x across GPT-style models of different sizes, with the largest gains coming from FP8. For a 7-billion-parameter model, the FP8 on the H100 was about 3x faster out of the box than BF16 on the A100. For most teams, the sensible expectation is about double the training throughput without extra engineering effort, with more available if you invest in FP8. If your team lacks in-house expertise to tune FP8, cloud consulting companies or DevOps service companies can help.
For inference, E2E Networks published vLLM benchmarks across three model sizes using real conversational data, and they show clearly how the H100 advantage changes with the model. E2E sells both GPUs, so treat its figures as one provider’s results rather than a neutral test.
On an 8B-parameter model, which fits easily in memory and is limited by computing, the H100 reached about 7,024 output tokens per second against 3,071 on the A100, a 2.29x improvement that approaches the theoretical compute gap of about 3.2x.
Under concurrent serving load, the H100 cut average time to first token from roughly 48 milliseconds to 29.5 milliseconds and nearly halved time per output token. Worst-case latency improved even more, with 99th-percentile time to first token dropping from about 192 milliseconds to 108 milliseconds. For chatbots and interactive tools, users notice that latency improvement far more than they notice a raw throughput number.
On a 32B-parameter model, which takes roughly 64 GB in BF16 and leaves little room on an 80 GB card, the story shifts. The H100’s BF16 advantage over the A100 shrank to about 1.8x because memory bandwidth and cache space, not compute, became the limit.
This is also where FP8 mattered most. Switching the H100 to FP8 shrank the model to about half its size, freed memory for larger batches, and lifted throughput from 1,251 to 2,795 tokens per second, a gain of more than 2x. Online latency for this model roughly halved as well, with time per output token falling from about 54 milliseconds on the A100 to between 20 and 26 milliseconds on the H100.
The practical lesson from these results is that the H100’s advantage is largest when you use what it uniquely offers. If you run it in BF16 on a workload that is bound by memory capacity, you get a modest gain. If you take advantage of FP8 and the Transformer Engine, you can double or triple the A100’s results.
Memory: Capacity, Bandwidth and the H200 Question
Memory sets hard boundaries on what you can run. A single 80 GB card can hold a model of roughly 30 billion parameters or fewer in BF16 with room left for the key-value cache and activations. A 70B model in BF16 needs about 140 GB for weights alone, which forces tensor parallelism across at least two 80 GB GPUs. A100 and H100 both face this limit, since both top out at 80 GB per card.
This is the gap the H200 fills. It uses the same Hopper chip as the H100, with identical compute, but pairs it with 141 GB of HBM3e memory running at 4.8 TB/s. It is therefore not a faster GPU for compute-bound jobs, where the H100 and H200 perform almost identically. It is a better GPU when memory is the bottleneck. In E2E tests, a 72B model could not run on two H100s because the weights consumed nearly all available memory and left a negative amount for the cache, so the team had to use four.
Two H200s handled the same model with room for longer context. Even though four H100s produced higher raw throughput, the two-H200 setup delivered about 18 percent better cost efficiency and a much simpler deployment.
If you are choosing between the A100 and the H100, the H200 matters mainly as a reminder that memory-hungry workloads such as 70B-plus models, long-context retrieval and large-batch serving may point you toward a different GPU than the one your compute needs suggest.
Where Blackwell Fits
NVIDIA’s Blackwell generation sits above Hopper. The B200 offers up to 192 GB of HBM3e at about 8 TB/s, roughly 2.4 times the H100’s capacity and more than twice its bandwidth, along with fifth-generation Tensor Cores that add FP4 support and NVLink at 1.8 TB/s per GPU. The B300 (Blackwell Ultra) raises memory to 288 GB. Power draw rises accordingly, with the B200 rated at up to about 1,000 W.
For most teams comparing the A100 and the H100, Blackwell is a different tier of budget and infrastructure rather than a direct alternative. It is aimed at frontier-scale training and high-concurrency inference on the largest models. The H100 remains the more established and generally cheaper option for models that fit within 80 GB to 141 GB, while Blackwell is worth pricing if you run very large models or need the highest per-GPU throughput.
Cost: Hourly Rate vs Total Job Cost
The H100 costs more per hour to rent, but the hourly rate is a misleading way to compare these GPUs. Cloud billing is based on how long your workload runs, and a faster GPU can finish the same job for less money.
Using E2E’s published rates as an example, an A100 80GB costs about $2.54 per hour (₹226) while an H100 costs about $2.80 per hour (₹249). Prices are as of October 2026 and change often. A fine-tuning job that takes 100 hours on an A100 would cost around $254 (₹22,600). The same job typically finishes in 40 to 50 hours on an H100, bringing the cost to roughly $112 and $140 (₹10,000 to ₹12,500).
| A100 80GB | H100 | |
|---|---|---|
| Hourly rate | $2.54 (₹226) | $2.80 (₹249) |
| Job duration | 100 hours | 40 to 50 hours |
| Total job cost | about $254 (₹22,600) | about $112 to $140 (₹10,000 to ₹12,500) |
The more expensive GPU ends up costing about half as much in total, because its speed advantage outweighs its price premium. Gcore’s published pricing for eight-GPU servers shows the same pattern at a different scale, with the H100 server priced about 60 to 65 percent higher per hour than the A100 server. That is a much bigger premium than the roughly 10 percent gap in E2E’s single-GPU rates because the two providers price different things: E2E’s figures are per-GPU instances; Gcore’s are whole eight-GPU servers, and each provider sets its own margins. The premium is more than covered if your job runs about 1.65 times faster or more. E2E and Gcore both sell these GPUs, so use their rates as illustrations and not as neutral market benchmarks.
There are important caveats. This logic only works when your software uses H100 strengths. A job limited by data loading, CPU preprocessing, or code that never touches FP8 may not speed up enough to justify the higher rate.
Prices also vary widely between providers, regions, and commitment terms, and spot or reserved pricing can change the calculation dramatically, with some providers offering spot H100 capacity at steep discounts to on-demand rates. Software licensing can be tied to specific GPU models and may cost more on newer hardware. You can compare providers side by side with HostingSeekers’ comparison tool.
How to benchmark your own workload
- Pick a representative job, such as a one-hour fine-tuning run or a fixed inference test set.
- Run it on both GPUs with the same batch size and software versions.
- Note the total run time and the hourly rate.
- Multiply the two to get the cost per completed job.
- Repeat in FP8 on the H100 if your framework supports it.
Power, Cooling and Infrastructure
The H100 SXM draws up to 700 W, compared with 400 W for the A100, an increase of 75 percent. Because it delivers more than three times the compute, its performance per watt is meaningfully better, so it can be the more efficient choice for compute-bound work even though it uses more power in total.
This matters mainly if you buy and house the hardware yourself, since higher draw means more heat and more demanding rack power and cooling requirements. In a public cloud, that burden falls on the provider. If you would rather not handle hardware at all, managed servers shift power, cooling, and maintenance to the provider, and server management companies can run self-owned hardware for you.
The form factor also matters. The PCIe version of the H100 is a cut-down design with 14,592 CUDA cores and slower HBM2e memory at about 2.0 TB/s, and its power limit falls to around 350 W. Its raw compute is roughly 24 percent lower than that of the SXM version, and for memory-bound inference the real-world gap can reach 40 to 60 percent. Always confirm which variant a provider is offering before you compare prices, because an H100 PCIe instance is not equivalent to an H100 SXM instance.
Where Do the L40 and L40S Fit?
Buyers often ask whether the L40 or the newer L40S is a cheaper alternative to the A100 or H100. They are better thought of as a different kind of GPU. Both are built on the Ada Lovelace architecture and use 48 GB of GDDR6 memory, not the high-bandwidth memory used in the A100 and H100. Both also have fourth-generation Tensor Cores and FP8 support. The L40S is the more AI-oriented of the two, with a 350 W power limit, and it is the card most buyers actually compare against the A100 for inference. Its memory bandwidth is 864 GB/s, well below the 2.0 TB/s of the A100.
They are oriented toward graphics, video, rendering, virtual workstations and lighter inference, and they are considerably cheaper and less power-hungry. They lack the memory bandwidth, the NVLink scaling and the training throughput that make the A100 and H100 suited to serious large-model work. For smaller inference deployments, media pipelines and mixed graphics-and-AI workloads, the L40S can be a sensible and economical choice. For training or serving large language models, neither is a substitute.
Which GPU for Which Use Case
| Use case | Best fit | Why |
|---|---|---|
| LLM training above 20 to 30B parameters | H100 | FP8 and the Transformer Engine deliver 2x to 3x the A100’s speed |
| LoRA and QLoRA fine-tuning up to 70B | H100 | Faster iteration and often a lower total job cost |
| Real-time chatbots and low-latency serving | H100 | Much lower time to first token and time per output token |
| Smaller models, classical ML, analytics | A100 | Memory and FP8 are not the limit, and rates are often lower |
| Batch inference with relaxed latency | A100 | Cost per hour matters more than speed |
| 70B-plus models, long context, large batches | H200 | 141 GB of memory avoids splitting across extra GPUs |
| Largest models and highest per-GPU throughput | Blackwell (B200/B300) | 192 GB to 288 GB of memory and FP4 support |
| Graphics, video, and lighter inference | L40S | Cheaper and less power-hungry, but not built for large-model work |
Final Verdict
The H100 is the stronger GPU in nearly every technical respect. It offers roughly three times the compute, much faster memory, FP8 and the Transformer Engine, a faster interconnect and confidential computing, and in practice it delivers two to three times the speed on most AI workloads, with more when your software is tuned for it. The A100 is still a capable, widely supported, and often cheaper option for smaller models and mixed workloads. With H100 prices having dropped, the old assumption that the A100 is always the budget-friendly choice no longer holds. The most reliable way to decide is to measure the cost of finishing your actual job on each GPU, not to compare hourly rates or spec sheets.
If you are moving existing workloads from A100 to H100, migration and deployment companies can handle the switch. Ready to test? Compare GPU server providers on HostingSeekers.
Frequently Asked Questions
How much faster is the H100 than the A100?
For most AI workloads, the H100 is two to three times faster. Independent training tests showed about 2.2x to 3.3x gains, and vLLM inference tests on an 8B model showed about 2.3x. Vendor figures of 4x or more usually assume FP8 and heavy optimization.
Does the H100 have more memory than the A100?
Not per card. Both offer 80 GB at the top end, but the H100 uses HBM3 with about 3.35 TB/s bandwidth compared with about 2.0 TB/s on the A100.
Does the A100 support FP8?
No. The A100 (Ampere) does not support FP8. Hopper GPUs such as the H100 and H200, Ada Lovelace GPUs such as the L40 and L40S, and Blackwell GPUs all do, and it is one of the biggest reasons the H100 pulls ahead on large language models.
Is the H100 cheaper than the A100 in total cost?
It can be. Its hourly rate is higher, but if it finishes a job in half the time or less, the total cost is often lower. This depends on your software making use of the H100 features.
Are the A100 and H100 both supported by MIG?
Yes. Both can be split into up to seven isolated instances, and the H100 offers a second-generation implementation.
What is the difference between H100 SXM and PCIe?
The SXM version has more CUDA cores, faster HBM3 memory, and higher power limits. The PCIe version has fewer cores, slower memory and lower power, and can be 40 to 60 percent slower on memory-bound inference.
Is the A100 discontinued?
NVIDIA has ended A100 production, so supply is finite. The GPU is still widely deployed and well supported in software.
Should I choose the H200 instead?
Only if memory is your bottleneck. The H200 has the same compute as the H100, so it pays off for 70B-plus models, long-context workloads, and large-batch serving, not for smaller compute-bound jobs.
H100 vs H200 vs B200: Which should I choose?
The H100 is the value pick for models that fit in 80 GB. The H200 has the same compute but 141 GB of memory, so it suits memory-bound work. The B200 is a newer Blackwell GPU with up to 192 GB of memory, about 8 TB/s of bandwidth and FP4 support, aimed at the largest models and highest throughput, and it usually costs more per hour.
Where can I rent H100 or A100 GPU servers?
Several cloud and dedicated server providers offer both. You can filter and compare them by price, location and specs on HostingSeekers’ GPU server listings.
