The H100 isn’t just faster than the A100; it fundamentally changes the equation for AI inference by delivering near-constant latency even under heavy load.
Let’s see this in action. Imagine you’re running a large language model, say, Llama 2 70B, for inference. On an A100, as more requests pile up, the latency for each request starts to creep up. You might see average latencies around 200ms, but at peak load, individual requests could easily hit 500ms or more. This variability is a killer for real-time applications.
Now, switch to an H100. Even with the same number of concurrent requests hitting it hard, the latency remains remarkably stable. You’ll still see averages in the 200ms range, but crucially, those peak latencies might only nudge up to 250ms. This consistent, low latency is a direct result of the H100’s architecture, specifically its enhanced Transformer Engine and larger, faster memory subsystem.
The problem the H100 solves, and where it dramatically pulls ahead, is in the inference phase of large AI models, particularly those based on the Transformer architecture. For years, the bottleneck in deploying these models wasn’t just raw throughput, but the predictability of their performance. As request volume increased, latency would spike, making real-time applications like conversational AI, real-time translation, or recommendation engines unreliable. The A100, while a powerhouse for training and a decent inference card, still exhibited this characteristic latency degradation.
The H100’s internal design is optimized for the specific computations prevalent in Transformers: matrix multiplications and attention mechanisms. NVIDIA introduced the "Transformer Engine" in the H100. This isn’t just a marketing term; it’s a hardware-software co-design feature. The Transformer Engine dynamically manages the precision of computations, switching between FP8 and FP16 formats on the fly. FP8 (8-bit floating point) offers a massive reduction in memory bandwidth and computational cost compared to FP16 (16-bit floating point), which was the typical lower bound for inference on previous generations. The H100’s Tensor Cores are specifically designed to accelerate FP8 operations, and the Transformer Engine intelligently decides when and where to use FP8 versus FP16 to maintain accuracy while maximizing speed and minimizing latency.
Furthermore, the H100 boasts significantly more HBM3 memory (up to 80GB) and much higher memory bandwidth (up to 3.35 TB/s) compared to the A100 (up to 40GB HBM2e and 1.55 TB/s). This is critical for large models that often don’t fit entirely into on-chip caches. Faster access to model weights and activations directly translates to lower latency, as the GPU spends less time waiting for data. The H100 also features a more advanced NVLink interconnect, enabling faster communication between GPUs in multi-GPU setups, which is crucial for scaling inference with very large models.
The key levers you control are primarily around model precision and batching strategy. For the H100, leveraging FP8 precision via the Transformer Engine is paramount. This is often managed by libraries like TensorRT-LLM. You can configure it to use different combinations of FP8 and FP16, balancing accuracy against performance. For example, a configuration might use FP8 for most matrix multiplications but switch to FP16 for specific layers or operations where precision is more critical. Batching also plays a role, but on the H100, the benefit of larger batch sizes is often seen in increased throughput with minimal latency degradation, whereas on the A100, large batches would lead to unacceptable latency spikes.
The most surprising aspect of the H100’s performance is how its architecture inherently prioritizes low-latency inference by aggressively managing computational precision. While previous GPUs focused on raw FLOPS and memory bandwidth for throughput, the H100’s Transformer Engine actively optimizes for the dynamic range of computations needed by modern AI models. It’s not just about doing more calculations per second; it’s about doing the right calculations at the right precision, often in FP8, to shave off nanoseconds from critical operations. This fine-grained control over precision, where the hardware and software collaborate to choose between FP8 and FP16 based on the specific tensor and its dynamic range, is what allows the H100 to maintain such consistent latency even under heavy concurrent load. This is a fundamental shift from simply throwing more compute at the problem to intelligently managing the computation itself.
The next hurdle you’ll face is optimizing for multi-GPU inference with the H100s, especially when dealing with models that exceed the memory of a single card.