H100 vs A100: Which NVIDIA GPU for LLM Training and Inference

The NVIDIA H100 and A100 have two generations between them, and yet they remain the two cards most Indian AI teams actually weigh when renting compute in 2026. The newest Blackwell parts get the headlines, but the actual budget decision on the ground is still H100 against A100.

This is a practical walk-through of that decision. 

Let’s start with a TL; DR: 

• Training: the H100 is the more dependable choice, and the margin is meaningful.

• Inference at scale or under production concurrency: the H100 is the stronger option.

• Small fine-tunes and latency-tolerant batch work: the A100 holds up, and costs less per hour.

• Mental model to consider: the H100 costs more per hour but finishes faster, so the right comparison is cost per finished job, not the hourly rate.

Specifications to start with

Source: NVIDIA H100 product page.

What to make out of it:

• Memory bandwidth is the clearest H100 advantage for inference, for the reason set out above.
  
• FP8 support is the H100’s defining addition. The A100 has no hardware equivalent, which is the single feature that justifies most of the price gap for transformer workloads.
  
• Memory capacity is the row most likely to mislead. Both cards list 80 GB, so the comparison looks like a tie on memory. It is not. The same capacity moves to the cores far faster on the H100.
  
• PCIe generation looks like a footnote and is anything but, for reasons covered in the cold-start section below.

Architecture

The two cards were designed for different worlds.

FP8 uses half the memory of BF16 per value, so the H100 processes roughly twice as many values per cycle on those layers. The A100 has no FP8 path at all and tops out at INT8 software quantization, with no hardware acceleration behind it.

So the H100 is not simply a faster A100. It carries a dedicated unit for the exact matrix maths that language models spend their time on, and the A100 does not. Most of the performance difference follows from that one design decision.

Training

For training, the H100 is the more reliable choice, and the answer is rarely close.

• The H100 trains transformer models roughly 2x to 3x faster than the A100 at comparable utilization.
  
• On workloads that can use FP8, the effective gap reaches 5x to 6x.
  
• A Lambda Labs benchmark recorded up to 8x with FlashAttention on a multi-GPU setup, and a little over 2x without it. The 8x figure is best read as a best-case ceiling rather than a number you will hit routinely.

Multi-GPU training widens the case further. Training parallelizes poorly across cards, and the H100’s faster NVLink (900 GB/s against 600) reduces the all-reduce communication stalls that erode scaling efficiency beyond four GPUs.

This also answers a question that recurs constantly in practitioner forums: whether to buy three H100s or five A100s for fine-tuning a 70B model. The instinct is to count VRAM and choose the five. In an r/MachineLearning thread on exactly this question, the more useful conclusion was that FP8 training on the H100s more than covers the memory deficit, and that the additional A100 capacity only helps if the job genuinely will not fit otherwise. For training above roughly 30B parameters, the H100s are the straightforward recommendation.

Inference: where the picture has more nuance

The training answer is clear. Inference depends on model size and, just as importantly, on load.

A 70B model in FP16 needs around 140 GB, which fits on neither card’s 80 GB. That leaves two options:

• Shard across two GPUs, or
  
• Quantise. INT8 brings a 70B to roughly 70 GB, which fits on a single card.

On that single-card INT8 setup, the H100’s bandwidth alone delivers faster tokens. Enabling FP8 then roughly doubles throughput on the transformer layers while halving memory pressure, which buys larger batches and longer context on the same card. The A100 cannot follow into FP8 territory.

The effect of concurrency is worth understanding clearly. In an r/MachineLearning discussion, a director of model serving at Databricks set it out plainly: larger batch sizes shift the bottleneck from memory bandwidth to compute, and the H100 has considerably more compute. The practical reading by load:

One trap is worth stating directly, because teams tend to learn it the expensive way. Two A100s are not equivalent to one larger GPU. The second card mainly adds memory that has to cross the bus, and a single inference pass through a transformer stack is sequential, so it does not parallelise cleanly. Where a model fits on one 80 GB H100, that single card generally serves it faster than two A100s. Fewer, faster cards carry less interconnect overhead.

The part that never reaches a datasheet: cold starts

This factor does not appear on any specification sheet, and teams usually discover it only after it has cost them.

A recent r/LocalLLaMA writeup benchmarked A100 and H100 nodes loading models from local NVMe across multiple GPUs:

The author’s first explanation was PCIe Gen4 versus Gen5. The thread pushed back correctly, since the raw lane maths does not support a 10x difference, and the real cause turned out to be more involved: CPU, root complex, NUMA layout and NVMe controller behavior under high queue depth. The Gen5 platform around the H100 simply had more aggregate I/O headroom to feed several cards loading at once.

The lesson generalizes well beyond that one benchmark:

• You do not rent “an H100”. You rent the whole server it sits in.
  
• Cold-start latency for large models is dominated by random disk reads, not GPU speed.
  
• The CPU, PCIe generation and NVMe layout decide whether a 70B model loads in seconds or stalls.
  
• If scale-to-zero or fast autoscaling matters to you, ask the provider what surrounds the GPU, not only which GPU it is.

A fast card in a slow box behaves like a slow box.

Software: the part that is refreshingly uneventful

On software compatibility, the news is good, and it is identical for both cards.

• The H100’s compute capability (9.0) is a superset of the A100’s (8.0).
  
• A100 containers run on the H100 with no modification.
  
• The same CUDA stack, drivers and tooling apply across both.

Moving up therefore costs nothing in engineering time. The only thing being traded is hourly rate for throughput. That asymmetry is a strong argument for treating the H100 as the default and reaching for the A100 only when there is a specific reason to.

Total cost of ownership

On hourly rate alone, the A100 is the cheaper card. That figure does not capture the decision, because the rate that matters is cost per finished job rather than cost per hour.

If the H100 costs more per hour but completes the work in a fraction of the time, the total bill is lower and the results arrive sooner. The A100 only wins on cost when the H100’s speed goes unused, which happens whenever something other than the GPU is the bottleneck:

In those situations, you would be paying for headroom you cannot use, and the A100 is the honest, cheaper choice.

Current Indian pricing on Neysa Velocis sets the two-card decision in context:

The more interesting comparison here is not the A100 at all, but the H200 in the row below. At roughly 8% more per hour than the H100, it offers 141 GB of HBM3e and higher bandwidth. For memory-hungry workloads chosen today, that is the upgrade most worth making, and arguably the comparison teams should be running instead of H100 versus A100. We have set that out in detail in our H100 vs H200 guide.

For teams that qualify, the IndiaAI Mission also offers subsidized H100 access to approved Indian startups and researchers at rates well below commercial pricing. Approval is not guaranteed and capacity tightens at peak demand, but where a project clears the bar, the economics move firmly toward the H100.

Prices move with availability and term. Treat these as a snapshot and confirm against the current Velocis pricing page before budgeting.

Where the A100 is the right choice

• Fine-tuning models below 30B parameters, where the H100’s speed advantage is modest relative to the savings.
  
• Batch inference that tolerates latency, where finishing a little later costs nothing.
  
• Pipelines bottlenecked outside the GPU, on data loading, preprocessing or storage, where a faster card cannot help.

Outside these cases, it is not the default recommendation.

Where the H100 is the clearer choice

• Training, particularly above eight GPUs, where NVLink 4.0 keeps scaling efficient.
• Production inference on transformer models, where bandwidth and FP8 compound.
• Any FP8-eligible workload, which the A100 cannot run in hardware at all.
• Anything where finishing faster outweighs a lower hourly bill, which describes most teams shipping to users.

This last group covers where most teams operate, which is why the H100 is the right answer for the majority of them. It is also the reasoning behind how we have built our own platform. Neysa Velocis offers H100 SXM, H100 NVL and H200 SXM as on-demand or reserved instances, from single-GPU virtual machines through to 8-GPU bare-metal nodes with 3,200 Gbps networking, with billing that carries no charge for data ingress, egress or inference transactions. The full breakdown is on the Velocis pricing page, so teams can match the GPU to the workload rather than to the marketing.

Concluding

The H100 is the sensible default. The A100 becomes the better choice when three conditions hold together:

1. The workload is a small fine-tune or latency-tolerant batch job rather than production serving.
2. The model sits comfortably below 30B parameters and does not need FP8.
3. The bottleneck is the GPU itself rather than data loading, storage or cold starts.

Where those conditions align, the A100’s lower hourly rate is a real saving. Where they do not, which is most of the time, the H100 finishes faster and usually costs less per job, and the hourly rate overstates the A100’s advantage.

The most useful habit, repeated by every experienced voice in those benchmark threads, is to rent both cards for a week, run your own workload, and measure. Public benchmarks rarely replicate exactly on your setup, and your job on your data is the only benchmark that ultimately counts.