NVIDIA A100 vs H100 vs H200: Which GPU Should You Choose?

Vishnu Subramanian

Founder @JarvisLabs.ai

For typical AI workloads, the H100 often hits the best balance of throughput and cost. A100 makes sense when budget matters more than speed. H200 is the right pick when your bottleneck is memory, whether that's model weights, KV cache, or batch size.

Quick Comparison

Bandwidth and memory type vary by form factor. The numbers above reflect the SXM variants unless noted otherwise. Note: H100 PCIe (80GB) uses HBM2e; H100 NVL (94GB, PCIe form factor) uses HBM3.

NVIDIA A100 Overview

The A100 runs on NVIDIA's Ampere architecture and set the standard for datacenter AI acceleration when it launched. It comes in 40GB (HBM2) and 80GB (HBM2e) configurations. The 80GB SXM variant delivers up to 2.0 TB/s of memory bandwidth, while the 40GB PCIe sits around 1.55 TB/s.

The A100 brought third-generation Tensor Cores and Multi-Instance GPU (MIG) support, which lets you partition a single GPU into up to seven isolated instances. It's been deployed at massive scale across cloud providers and enterprises, so the software ecosystem is mature and well-optimized.

For most practical AI work, the A100 still handles the job. Lower hourly rates make it a natural fit for development, experimentation, and batch processing where you're optimizing for cost rather than time-to-completion.

NVIDIA H100 Overview

The H100 uses NVIDIA's Hopper architecture and represents a generational jump from A100. The SXM variant has 80GB of HBM3 memory with 3.35 TB/s bandwidth. The PCIe variant uses HBM2e at 2.0 TB/s, so form factor matters when comparing specs.

What changed from A100 to H100:

• Fourth-generation Tensor Cores
• Transformer Engine that dynamically switches between FP8 and FP16
• Native FP8 precision for inference
• 80 billion transistors on TSMC 4N process (vs 54 billion on 7nm for A100)

The Transformer Engine is designed to accelerate transformer-based models by automatically managing precision. For LLM training and inference, this translates to real speedups, though the exact gain depends heavily on your specific workload, batch size, sequence length, and whether you're using FP8.

Performance uplift over A100 varies widely. FP8-friendly transformer workloads can see substantial gains. Memory-bound workloads may see smaller improvements. Marketing materials cite large multiples, but those typically assume optimal conditions and specific model architectures.

NVIDIA H200 Overview

The H200 shares the same Hopper architecture as H100 but attacks the memory bottleneck directly. With 141GB of HBM3e memory and 4.8 TB/s bandwidth, it holds larger models and batches without distributing across multiple GPUs.

The math compared to H100:

• 76% more memory (141GB vs 80GB)
• 43% higher memory bandwidth (4.8 TB/s vs 3.35 TB/s)
• Same compute characteristics, same Tensor Cores, same Transformer Engine

If your H100 workload leaves memory headroom, you won't see meaningful benefits from H200. But if you're constantly hitting memory limits, splitting models across GPUs, or constrained on batch size for inference throughput, the extra capacity matters.

When to Choose Each GPU

A100 Works Well When

Your budget is the primary constraint. A100 instances cost significantly less per hour than H100 or H200. If you're doing development work, running experiments, or processing batch jobs where completion time is flexible, the lower rate adds up.

Your models are already optimized for A100. Many popular models have been tuned specifically for A100 over the past several years. If you're using one of these, you get mature optimizations out of the box.

Your workload fits in 40GB or 80GB. If memory isn't your bottleneck, you're paying for headroom you don't need on newer GPUs.

H100 Works Well When

You're building production inference systems where latency affects user experience. The performance improvement translates to faster responses.

You're training transformer models and can take advantage of the Transformer Engine and FP8 support. The speedups for these architectures are real.

Time-to-completion matters more than cost per hour. If faster training compresses your iteration cycle, the productivity gain can outweigh the higher rate.

H200 Works Well When

Memory capacity is genuinely your bottleneck. 141GB handles models and batch sizes that would require multi-GPU setups on H100.

You're running large context windows for LLM inference. Long contexts need proportionally more KV cache, and that memory has to live somewhere.

You want to avoid multi-GPU complexity. Keeping everything on one card simplifies your infrastructure and eliminates communication overhead.

Working with very large models benefits from H200, especially with quantization. A 70B model in FP16 is on the order of ~140 GB (decimal) for weights alone (~130 GiB), so a 141GB H200 can still be tight once you include runtime overhead and KV cache. In practice, FP8/INT8/4-bit quantization and/or shorter contexts are often needed for comfortable single-GPU serving.

GPU Pricing and Availability

Check our pricing page for current rates on A100, H100, H200, and other GPUs. Pricing changes as availability shifts, so the pricing page has the most accurate numbers.

The price gap between A100 and H100 often closes when you factor in performance. If H100 finishes a training run in 4 hours instead of 10, the total cost might be comparable despite the higher hourly rate. For inference, higher throughput means more tokens per dollar.

Practical Recommendations

For most teams, H100 hits the sweet spot between performance and cost for production workloads. The Transformer Engine and FP8 support align well with current model architectures, and the performance gains are meaningful for transformer-heavy work.

A sensible workflow: use A100 for development and experimentation where you're iterating quickly and cost matters, then move to H100 for production where performance matters. This gives you the best of both.

Consider H200 when you're genuinely memory-constrained. If you're already running H100 with memory headroom to spare, upgrading won't help. But if memory limits are forcing you into multi-GPU setups or constraining your batch sizes, H200's 141GB can simplify things significantly.

FAQ

Is H100 worth the premium over A100?

For production workloads, often yes. The performance improvement can mean lower total costs despite the higher hourly rate. For development and batch processing where time is flexible, A100 usually offers better value.

Can I run the same models on all three GPUs?

Yes, they all support the same CUDA ecosystem. The differences are performance, memory capacity, and hardware features. To get the full benefit of H100 and H200 features like FP8 and Transformer Engine, you need compatible framework and library versions.

What's the main advantage of H200 over H100?

Memory. H200 has 141GB versus H100's 80GB, with 43% higher bandwidth. Compute performance is similar since both use Hopper architecture. Pick H200 when your workload is memory-bound.

Which GPU is best for fine-tuning LLMs?

H100 handles most fine-tuning well. The Transformer Engine accelerates the attention computations that dominate fine-tuning. For very large models where memory becomes the constraint, H200's additional capacity helps.

What about the H100 NVL variant?

The H100 NVL is a PCIe form factor with 94GB of HBM3 and 3.9 TB/s bandwidth. It sits between the standard H100 (80GB) and H200 (141GB) on memory capacity. Availability varies by provider.

How do I decide between 40GB and 80GB A100?

40GB is a solid starting point for 7B-13B-class fine-tunes in many setups, and it can go higher with careful choices (QLoRA/4-bit, shorter sequence lengths, smaller batches, optimizer/activation tricks). If you need longer contexts, higher batch sizes, or more headroom, 80GB is the safer choice.