H100 vs A16
Explore a head to head comparison of specifications, performance, and pricing.
H100
The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.
A16
The NVIDIA A16 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H100 vs A16: Which Should You Choose?
The H100 offers 80 GB of VRAM — 1.3× the 64 GB on the A16 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H100 delivers 267.6 TFLOPS versus 4.493 TFLOPS on the A16 — 60× faster for mixed-precision training and inference. Memory bandwidth favors the A16 at 0.00 TB/s compared to 0.00 TB/s on the H100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the H100 is built on Hopper while the A16 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the A16 starts from $0.51/hr versus $1.66/hr for the H100 — 225% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 1 for the A16, giving more options for region and pricing flexibility.
H100 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H100 when:
- ✓You need 80 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
A16 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A16 when:
- ✓64 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
See how the H100 & A16 compare
Compare detailed hardware specifications and average pricing for the H100 and A16.
Compare Hardware Specifications
| H100 | A16 | |
|---|---|---|
| GPU Type | H100 | A16 |
| VRAM per GPU | 80 GB | 64 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Ampere |
| Interconnect | PCIe Gen5 or SXM5 | PCIe Gen4 |
| Memory Bandwidth | 3.35 TB/s | 4x 200 GB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 4.493 TFLOPS (1:1) |
| CUDA Cores | 16896 | 4x 1,280 |
| Tensor Cores | 528 (4th Gen) | 4x 40 (3rd Gen) |
| RT Cores | N/A | 4x 10 (2nd Gen) |
| Base Clock | 1365 MHz | 1312 MHz |
| Boost Clock | 1785 MHz | 1755 MHz |
| TDP | 350-700W | 250W |
| Process Node | TSMC 4N | TSMC 8nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| H100 | A16 | |
|---|---|---|
| 1 GPU | $3.03 /hr | $0.51 /hr |
| 2 GPUs | $5.61 /hr | $1.02 /hr |
| 4 GPUs | $10.46 /hr | $2.05 /hr |
| 8 GPUs | $20.15 /hr | $4.09 /hr |
Frequently Asked Questions: H100 vs A16
The main differences are VRAM (80 GB vs 64 GB), FP16 throughput (267.6 vs 4.493 TFLOPS), architecture (Hopper vs Ampere). The H100 uses the Hopper architecture while the A16 is based on Ampere, giving each GPU different generational capabilities.
The H100 is generally better for large language model training due to its higher throughput and 80 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the A16 is available from $0.51/hr. The H100 starts from $1.66/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The H100 has more VRAM at 80 GB, compared to 64 GB on the A16. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H100 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A16, paying the premium may be justified by faster job completion and lower total cost.
The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 1 for the A16. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either H100 or A16. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore H100 & A16 Instances
Browse available instances with H100 and A16 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
Explore more GPU comparisons
Select any two GPUs to compare their specifications and explore pricing across providers.
