H100 vs A100: Specs, Benchmark, Speed, Memory, and Rental Cost
NVIDIA H100 and A100 are datacenter GPUs for training, fine-tuning, and inference, but they target different performance and budget tiers. This comparison uses H100 SXM and A100 80 GB SXM specifications so memory capacity, bandwidth, tensor throughput, and interconnect are compared on a consistent basis.
H100 vs A100 specifications
| Specification | H100 SXM | A100 80 GB SXM |
|---|---|---|
| Architecture | Hopper | Ampere |
| GPU memory | 80 GB HBM3 | 80 GB HBM2e |
| Memory bandwidth | 3.35 TB/s | 2.039 TB/s |
| TF32 Tensor Core, with sparsity | 989 TFLOPS | 312 TFLOPS |
| BF16 / FP16 Tensor Core, with sparsity | 1,979 TFLOPS | 624 TFLOPS |
| FP8 Tensor Core, with sparsity | 3,958 TFLOPS | Not supported |
| NVLink | 900 GB/s | 600 GB/s |
| MIG support | Up to 7 instances | Up to 7 instances |
| Maximum TDP | Up to 700 W | 400 W standard SXM configuration |
These are NVIDIA peak specifications, not an application benchmark. H100 provides about 1.64 times the memory bandwidth and about 3.17 times the sparse TF32 or BF16/FP16 tensor throughput of A100 SXM. Real training and inference speed depends on precision, model architecture, batch size, framework kernels, sequence length, communication overhead, and GPU utilization.
Specifications vary by PCIe, SXM, and NVL form factor. Check the exact GPU attached to a rental listing before comparing price or expected throughput.
H100 vs A100 performance for AI workloads
LLM training and fine-tuning
H100 has the stronger ceiling when a training stack can use Hopper Tensor Cores, FP8, fast interconnect, and large batches. A100 remains a practical value choice when the model already fits in 80 GB, the job is not deadline-sensitive, or the software path does not use H100-specific acceleration.
LLM inference and serving speed
H100 is usually the better fit for high concurrency, latency-sensitive inference, and long-context serving because tensor throughput and memory bandwidth provide more headroom. A100 is often sufficient for validation, lower-traffic endpoints, offline batch inference, and workloads where utilization matters more than peak throughput.
Memory capacity and long context
Both compared SXM variants have 80 GB of GPU memory, so H100 does not automatically fit a model that cannot fit on A100 80 GB. H100's advantage is faster HBM3 bandwidth and newer compute features. Model weights, KV cache, activation memory, batch size, and runtime overhead still decide whether 80 GB is enough.
Which GPU should you choose?
Choose A100 when 80 GB of VRAM is the main requirement and predictable fine-tuning, notebooks, or validation matter more than maximum throughput. Choose H100 when training speed, long-context inference, high concurrency, or FP8 support can reduce total job time enough to justify the premium.
- Fine-tuning: A100 is often the value baseline; H100 is useful when turnaround time is the constraint.
- LLM inference: H100 is better suited to high-throughput and latency-sensitive serving.
- Experiments: Both may be excessive when an RTX 4090 or hosted model API fits the workload.
- Long context: benchmark production-shaped sequences because KV cache and concurrency can dominate memory use.
H100 vs A100 rental cost in India
Do not compare only the hourly price. Multiply the live INR rate by model loading, training, checkpointing, retries, and expected completion time. A faster H100 can cost less for a throughput-bound job, while A100 usually wins when the workload already meets its deadline.
Compare live H100 and A100 availability Plan GPU rental pricing in India Estimate GPU memory requirementsH100 vs A100 frequently asked questions
Is H100 faster than A100?
Yes on peak tensor throughput and memory bandwidth, but the real speedup depends on whether the workload uses the supported precision, optimized kernels, batching, and enough work to keep the GPU busy.
Does H100 have more memory than A100?
Not in this like-for-like comparison: H100 SXM and A100 80 GB SXM both provide 80 GB. H100 uses faster HBM3 memory, while A100 80 GB uses HBM2e.
Is H100 worth the higher rental price?
It is worth testing when shorter completion time, higher throughput, or lower latency offsets the premium. If A100 already meets the deadline and capacity target, A100 can deliver a lower total job cost.