In India right now, an H100 costs ₹191–362/hr on-demand depending on the provider. An A100 80GB runs ₹115–189/hr. The H100 carries roughly a 1.5–2× price premium. Whether that premium is worth paying depends entirely on your workload — and for a majority of Indian AI teams, it is not.
Where the H100 clearly wins
For large-scale pretraining runs on models above 13B parameters, the H100's HBM3 memory bandwidth (3.35 TB/s vs the A100's 2 TB/s) and FP8 support translate directly to faster training at lower per-token cost. If you're running a 70B or 405B model training job where the GPU is the bottleneck 100% of the time, the H100's throughput advantage typically more than covers the price difference. Multi-GPU NVLink scaling is also significantly better on H100 clusters — important for runs across 8 or more GPUs where interconnect bandwidth limits utilization on A100 configurations.
Where the A100 is the smarter choice
For inference serving of models up to 34B parameters, for fine-tuning on existing open-source models, and for any workload where the GPU sits at less than 80% utilization, the A100 is almost always the better economic decision. Fine-tuning Llama 3.3 70B with QLoRA fits comfortably in an A100 80GB's VRAM, takes roughly the same wall-clock time as an H100 fine-tune at 4-bit precision, and costs 40–50% less. For RAG pipelines and embedding generation, the L40S or even the A30 outperforms both on cost-per-inference because those workloads are memory-bound rather than compute-bound.
The VRAM question
Both the H100 SXM and A100 are available in 80GB configurations from most Indian providers. If your model fits in 80GB, you are not compute-constrained by either GPU for most inference workloads. Where the H100's faster memory bandwidth matters is when you're saturating the GPU with continuous high-batch-size inference — for example, a production API serving thousands of concurrent users. At Indian pricing and typical Indian startup traffic levels, most teams hit cost constraints long before they hit H100-class throughput requirements.
Practical recommendation
Start with the A100 80GB unless you have a specific benchmark showing your workload is compute-saturated on A100. The cheapest A100 80GB in India from a provider with an Indian GST invoice is NeevCloud at ₹126/hr. If a training run is taking three times longer than expected and profiling shows consistent 95%+ GPU utilization, upgrade to H100 for that specific job. Don't provision H100s as your default just because they are faster — faster hardware running at 40% utilization is slower money than slower hardware running at 90%.