Choosing between the NVIDIA A100 and H100 isn't simply a question of picking the newer GPU.
The H10Choosing between the NVIDIA A100 and H100 isn't simply a question of picking the newer GPU.
The H100 is faster. But it's also more expensive.
For AI teams running LLM inference, fine-tuning, or production workloads, the real question is:
Does the H100's additional performance justify its higher hourly cost for your workload?
A100 vs H100: What's the Difference?
Both GPUs offer 80GB-class memory on their SXM variants, but the underlying architectures are very different.
The A100 is based on NVIDIA Ampere and uses HBM2e memory with around 2 TB/s of bandwidth. The H100 uses Hopper architecture, HBM3 memory with 3.35 TB/s bandwidth, and NVIDIA's Transformer Engine for optimized FP8 workloads.
In simple terms:
- Architecture: A100 → Ampere | H100 → Hopper
- Memory: A100 → 80GB HBM2e | H100 → 80GB HBM3
- Memory bandwidth: A100 → ~2.0 TB/s | H100 → ~3.35 TB/s
- FP8 support: A100 → No | H100 → Yes, with Transformer Engine
- Best suited for: A100 → Cost-efficient AI workloads | H100 → High-throughput AI workloads
The difference becomes especially important for LLM inference, where memory bandwidth and throughput can directly affect how many tokens your GPU can generate.
Is the H100 Worth the Extra Cost?
This depends on your workload.
At current Packet.ai pricing, an A100 starts at $1.43/GPU-hour, while the H100 is listed at $2.50/GPU-hour.
That's roughly a 75% higher hourly cost for the H100.
But higher GPU cost doesn't automatically mean higher inference cost.
If your workload is highly concurrent, uses larger models, or benefits from FP8 acceleration, the H100's higher throughput can potentially produce more tokens per dollar.
For smaller models, experimentation, moderate inference workloads, or cost-sensitive fine-tuning, an A100 can be the more economical choice.
A Simple Rule of Thumb
Choose A100 if:
- You're optimizing for GPU cost
- You're fine-tuning smaller or mid-sized models
- Your inference workload has moderate concurrency
- You don't need FP8 acceleration
- You want 80GB of VRAM without paying for Hopper-class performance
Choose H100 if:
- You're serving LLMs at high concurrency
- Throughput and latency are critical
- You're running larger transformer workloads
- You want native FP8 acceleration
- Higher GPU utilization can offset the additional hourly cost
The important lesson is that the fastest GPU isn't always the cheapest GPU to run.
Your decision should be based on cost per useful output such as tokens generated, training hours saved, or jobs completed - not simply $/GPU-hour.
Want the Full A100 vs H100 Breakdown?
We've compared the architecture, memory bandwidth, inference throughput, pricing, fine-tuning economics, and workload-specific use cases in our complete guide:
NVIDIA A100 vs H100 in 2026: Price, Performance and Which GPU Fits Your Workload
If you're choosing a GPU for your next AI workload, that's the comparison worth reading before you start paying for compute.0 is faster. But it's also more expensive.
For AI teams running LLM inference, fine-tuning, or production workloads, the real question is:
