A100 vs H100 for LLM Inference: The Engineer’s Guide to Efficiency
Choosing the right architecture for throughput, latency, and cost-effective scaling in 2026.
Felix Seifert
January 19, 2026 · Head of Engineering at Lyceum Technologies
Choosing between the NVIDIA A100 and H100 is no longer just a question of budget. For engineers building the next generation of AI applications, it is a choice between two fundamentally different architectural approaches to the transformer block. The A100 was the workhorse of the first LLM wave, but the H100 was built specifically to solve the bottlenecks that emerged during that era. At Lyceum Technology, we see teams struggling with OOM errors and high latency because they are trying to force modern, high-parameter models onto older hardware without considering the total cost of inference. This guide breaks down the technical reality of these GPUs to help you optimize your deployment.
The Architectural Leap: Why the Transformer Engine Changes Everything
When we look at the transition from the Ampere architecture (A100) to the Hopper architecture (H100), the most significant advancement for inference is the Transformer Engine. This is not just a minor speed bump. It is a specialized hardware and software layer designed to accelerate the very math that powers Large Language Models. While the A100 relies on FP16 or BF16 precision for most workloads, the H100 introduces native FP8 support. This allows the GPU to process data using 8-bit floating-point numbers without a significant loss in model accuracy.
FP8 Precision and the Transformer Engine
The impact on memory and compute is massive. By using FP8, you effectively double the throughput compared to FP16 because you are moving half the data across the memory bus for the same number of parameters. According to NVIDIA's 2025 technical documentation, the H100 can deliver up to 9x more throughput in specific AI training scenarios, but for inference, the real-world gain typically sits between 3x and 4x for models like Llama 3 or Mistral Large.
Consider the attention mechanism. In a standard transformer block, the KV (Key-Value) cache grows linearly with sequence length. On an A100, long-context windows often lead to Out-of-Memory (OOM) errors because the memory bandwidth cannot keep up with the demand. The H100 addresses this with significantly higher memory bandwidth (3.35 TB/s vs 2.0 TB/s) and fourth-generation NVLink, which allows for faster communication between GPUs in a cluster. If you are running models with 128k context windows, the H100 is not just faster, it is often the only viable way to maintain interactive latency.
Throughput and Latency: Real-World Benchmarks
Raw TFLOPS are a vanity metric. What matters to an ML engineer is tokens per second per user and total system throughput. In our internal testing at Lyceum, we have observed that the H100 consistently maintains lower latency even as batch sizes increase. This is a critical distinction for production environments where traffic is unpredictable.
Batch Size Impact on Throughput
For a model like Llama 3 70B, an 8x A100 node might struggle to maintain 20 tokens per second when serving multiple concurrent users. In contrast, a 4x H100 setup can often exceed 60 tokens per second for the same workload. This is due to the H100's ability to handle larger batch sizes more efficiently. When you increase the batch size on an A100, you quickly hit a wall where the compute cores are waiting for data from memory. The H100's increased memory bandwidth ensures those cores stay fed.
Small Models (7B - 14B)
The A100 is still a powerhouse here. If your latency requirements are flexible, the A100 provides excellent value.Medium Models (30B - 70B)
This is the tipping point. The H100's FP8 capabilities make it significantly more efficient for real-time chat applications.Large Models (100B+)
The H100 is mandatory. The inter-GPU communication speed provided by NVLink 4 is necessary to prevent the interconnect from becoming the primary bottleneck.
A common mistake we see is engineers choosing the A100 because the hourly rental price is lower. However, if the H100 is 3x faster, you only need one-third of the time to process the same number of requests. In many cases, the cost per 1 million tokens is actually lower on the H100 despite the higher sticker price.
The Cost-Efficiency Matrix: When to Stick with A100
We are radically transparent about this: the H100 is not always the right choice. There are specific scenarios where the A100 remains the more strategic asset, especially for European startups mindful of their burn rate. If your workload is batch-oriented rather than real-time, the latency benefits of the H100 might not translate into business value.
For example, if you are running offline document processing, sentiment analysis on historical data, or any task where the user isn't waiting for a cursor to blink, the A100's lower cost per hour makes it highly attractive. You can spin up a cluster of A100s, saturate them with a massive batch, and let them run. The price-to-performance ratio for non-interactive workloads often favors the older architecture.
Another factor is availability. While Lyceum Cloud prioritizes sovereign European H100 capacity, the global market for H100s remains tight. If you need to scale a cluster instantly and H100s are at 100% utilization, the A100 is a reliable fallback that still outperforms almost everything else on the market. We recommend a hybrid orchestration strategy: use H100s for your customer-facing inference APIs and A100s for your background processing and fine-tuning tasks.
Sovereignty and Infrastructure: Beyond the GPU
At Lyceum Technology, we believe that hardware is only half the battle. The other half is where that hardware sits and how it is managed. For European enterprises, running LLM inference on US-based hyperscalers introduces risks regarding data sovereignty and compliance. When you deploy an H100 on Lyceum Cloud, you are running on European soil under European jurisdiction.
Our orchestration layer, Protocol3, abstracts the complexity of these GPUs. You shouldn't have to worry about configuring CUDA versions or optimizing NCCL for NVLink. Whether you choose an A100 or an H100, our platform automates the hardware configuration. We use a Predictor tool to analyze your model's requirements and suggest the most efficient GPU type and count, preventing the common pitfall of over-provisioning.
The strategic importance of this cannot be overstated. As AI becomes the core of the modern enterprise stack, the infrastructure must be as resilient as the models themselves. By choosing a sovereign cloud provider that offers both A100 and H100 capacity, you ensure that your AI roadmap is not dependent on the shifting political or economic landscape of a single foreign region. Efficiency is not just about TFLOPS, it is about operational autonomy.
Decision Framework: A100 vs H100
To simplify your decision, we have developed a framework based on three primary pillars: Latency Sensitivity, Model Size, and Budget Constraints. If your application requires sub-second response times for a model larger than 30B parameters, the H100 is the clear winner. The architectural advantages of the Hopper generation are specifically tuned for these high-pressure environments.
However, if you are working with a limited budget and your models are highly optimized (e.g., heavily quantized 7B models), the A100 provides a stable, cost-effective platform. Many engineers find that they can achieve sufficient performance on A100s by using advanced inference frameworks like vLLM or TGI, which maximize the utilization of the Ampere cores.
Assess your model size
Anything over 70B parameters should ideally run on H100s to utilize NVLink 4.Define your latency SLA
If you need < 50ms per token, the H100's memory bandwidth is essential.Calculate TCO
Don't look at the hourly rate. Look at the cost to process 1,000 requests.
Ultimately, the goal is to avoid the DevOps friction that comes with manual infrastructure management. Our VS Code extension and one-click deployment tools are designed to let you switch between these GPU types as your needs evolve, ensuring you always have the right tool for the job without the migration headache.
