Should I Run Llama-405B on an NVIDIA H100 or A100 GPU?

Vishnu Subramanian

Founder @JarvisLabs.ai

H100s provide approximately 2-3x faster inference for Llama 405B compared to A100s, with H200s offering even better performance. For a model this massive, you'll need multiple GPUs regardless of choice. Choose H100/H200 for maximum throughput and efficiency; choose A100 for better cost-efficiency when speed isn't the primary concern.

Memory Requirements: The 405B Challenge

Let's address the elephant in the room first: Llama 405B won't fit on a single GPU of any type without significant compromises.

With 405 billion parameters, you're looking at roughly 810GB of memory in FP16 format—far exceeding even the newest H200's 141GB VRAM. This means:

• Multi-GPU deployment is mandatory: You'll need tensor parallelism across multiple devices
• Quantization is essential: Even with multiple GPUs, you'll want INT8/INT4 quantization
• Bandwidth becomes critical: Inter-GPU communication can become a bottleneck

I learned this firsthand when scaling our inference servers at Javis Labs. The jump from 70B to models >400B parameters isn't just linear—it introduces new architectural challenges.

Performance Comparison

When running massive models like Llama 405B, the H100's advantages become even more pronounced:

For a model this size, the H100's approximately 2.5x speedup over A100 can translate to real business value—cutting inference times from seconds to milliseconds, or reducing your cluster size substantially.

The H200 pushes this advantage even further with its massive 141GB VRAM capacity and enhanced memory bandwidth, which is particularly valuable for these ultra-large models.

Cost Analysis

Looking at JarvisLabs.ai pricing:

While H100/H200 costs more per hour, the overall economics might favor them depending on your use case:

• If latency matters: The cost premium is justified by faster responses
• If throughput is key: Fewer H100s can match the throughput of more A100s
• For 24/7 deployments: The efficiency gains compound over time

When to Choose H100/H200

I'd recommend H100 or H200 if:

• You need real-time responses: User-facing applications where milliseconds matter
• You're processing massive throughput: The efficiency gains scale with volume
• You want to minimize GPU count: Fewer, more powerful GPUs simplify infrastructure
• You need the extra memory: H200's 141GB VRAM allows for larger batch sizes

When to Choose A100

The A100 remains compelling if:

• Budget constraints are significant: You need more inference capacity per dollar
• Your workloads are bursty/sporadic: The cost gap matters more for intermittent usage
• You can parallelize effectively: Your architecture efficiently leverages multiple cheaper GPUs
• You've optimized for A100: Your pipeline is already tuned for A100 performance profiles

My Recommendation

Having bootstrapped our GPU cloud at Javis Labs, I've seen both sides of this equation. For Llama 405B specifically:

If you're serving real-time inference at scale, H100s provide better economics when you factor in the total cost of operations—not just the hourly GPU rate. We've seen customers cut their overall costs by 30% despite paying more per GPU hour.

For research and development where time-to-result isn't critical, A100s still offer tremendous value. We maintain a mixed fleet ourselves—H100s/H200s for production and A100s for internal development.

The constraint that often surprises teams isn't computation but memory. With 405B parameters, you're dealing with substantial memory requirements even after quantization. H200s shine here with their 141GB VRAM compared to H100's 80GB or A100's 40GB.

Remember that with JarvisLabs, you can start with A100s during development and easily switch to H100s/H200s when deploying to production—testing both approaches costs less than overprovisioning from the start.

What's your specific inference workload pattern? I'd be happy to help you think through the tradeoffs for your particular use case.