Why Choose an NVIDIA H100 Over an A100 for LLM Training and Inference?

Vishnu Subramanian

Founder @JarvisLabs.ai

The H100 delivers 2-3x faster LLM training and up to 30x faster inference compared to A100, with recent cloud pricing drops making it increasingly cost-effective for transformer-based workloads.

As someone who's been optimizing GPU infrastructure at Javis Labs for the past few years, I've seen firsthand how the choice between H100 and A100 can make or break LLM projects. The landscape has shifted dramatically in 2025—what once seemed like a simple cost vs. performance trade-off has evolved into something more nuanced.

Architectural Advantages for Transformers

The H100 wasn't just an incremental upgrade; it was specifically designed with large language models in mind. Here's what makes the difference:

Transformer Engine with FP8 Support: The H100's killer feature is its dedicated Transformer Engine that supports FP8 precision, which the A100 lacks entirely. This allows for faster computations without compromising model accuracy—essentially giving you the performance of lower precision with the quality of higher precision.

Fourth-Generation Tensor Cores: The H100 features fourth-generation Tensor Cores that deliver up to 4x the performance compared to the A100's third-generation cores. For transformer architectures, this translates to significant speedups in the attention mechanisms that dominate LLM computations.

Memory Bandwidth: The H100's 3.35 TB/s memory bandwidth (compared to A100's 2 TB/s) significantly impacts AI workloads, enabling faster weight updates and larger batch sizes—crucial when you're trying to maximize throughput on massive models.

Real-World Performance Numbers

Let's cut through the marketing hype and look at actual benchmarks. Independent testing shows:

• Training Speed: H100 GPUs achieved up to 3 times faster training times compared to A100 GPUs
• Inference Performance: According to NVIDIA's own benchmarks, the H100 offers up to 30X better inference performance than the A100, though independent sources suggest more realistic 10-20x gains
• Specific Models: The 30B model experienced a 3.3x increase in speed compared to the A100 when optimized for H100

From our internal testing at Javis Labs, we consistently see 2-3x improvements in training throughput for models like Llama 70B and similar-sized transformers.

The Cost Equation Has Changed

Here's where 2025 gets interesting. The H100's pricing advantage has dramatically improved:

H100 cloud pricing has plummeted from $8/hour to $2.85-$3.50/hour due to increased availability and provider competition. This shift has essentially eliminated the A100's previous cost advantage.

Our Javis Labs pricing reflects this market change:

• H100: ₹242.19/hour ($2.99/hour)
• A100: ₹104.49/hour ($1.29/hour)

While the H100 costs about 2.3x more per hour, it delivers 2-3x the performance. When factoring in the performance gains, training such a model on a pod of H100s can be up to 39% cheaper and take up 64% less time to train.

Memory and Model Size Considerations

For large language models, memory efficiency becomes critical:

80GB Sweet Spot: Both H100 (80GB) and A100 (80GB) offer the same VRAM, but the H100's improved memory bandwidth means better utilization of that capacity.

Quantization Benefits: With quantization techniques like 8-bit compression, you can fit Llama 70B on a single GPU. However, the H100's FP8 support provides built-in quantization capabilities that maintain higher quality than traditional methods.

Multi-GPU Scaling: For models exceeding 80GB, the H100's improved NVLink (900 GB/s vs 600 GB/s) provides better scaling across multiple GPUs.

When H100 Makes Sense

Choose H100 for LLM workloads when:

• Production Inference: User-facing applications where response latency directly impacts experience
• Large Model Training: Working with 70B+ parameter models where every hour saved matters
• Research Iteration: When you need to run multiple experiments quickly
• Real-time Applications: Conversational AI, live translation, or other latency-sensitive use cases
• Future-proofing: Planning for larger models and FP8-optimized frameworks

When A100 Still Works

The A100 remains viable for:

• Budget-Constrained Projects: When upfront costs matter more than time-to-completion
• Smaller Models: Sub-30B parameter models where the performance gap is less pronounced
• Batch Inference: Non-real-time workloads where you can sacrifice latency for cost
• Mixed Workloads: If you're running both LLM and non-transformer workloads

My Bootstrap Perspective

Having built Javis Labs without traditional VC funding, I understand the tension between performance and cost. Here's my take:

If you're serious about LLMs and can absorb the 2.3x hourly cost difference, the H100 pays for itself through faster iteration cycles and better user experiences. We switched our primary LLM inference to H100s last year and haven't looked back.

However, if you're just getting started or experimenting, A100s provide an excellent learning platform. You can always migrate to H100s once you've validated your approach and need production-scale performance.

The Bottom Line

The H100 vs A100 decision isn't just about raw performance anymore—it's about time-to-market, user experience, and total cost of ownership. With H100 pricing becoming more accessible and the architectural advantages for transformers being so pronounced, it's becoming the default choice for serious LLM work.

What's your specific use case? Knowing your model sizes, latency requirements, and budget constraints can help determine which GPU architecture will serve you best. Feel free to reach out—we've probably run similar workloads and can share our learnings.

Frequently Asked Questions

Q: Can I run Llama 70B on a single H100 or A100? A: With quantization (INT8 or FP8), yes. The H100's native FP8 support provides better quality-performance trade-offs than traditional quantization on A100.

Q: Is H100 availability still an issue in 2025? A: The H100 is becoming increasingly available due to improved production and competition among providers, while A100 availability may be limited as the industry shifts focus to newer releases.

Q: What about the upcoming B200 GPUs? A: While B200s are expected in early 2025, H100s will remain highly relevant given their proven performance and broader ecosystem support. Don't wait for next-gen hardware if you have immediate LLM needs.