The public conversation about Artificial Intelligence often focuses on the visible: models that write, generate images, or program. But beneath the surface, in the machinery room, the real discussion is different: which GPU to purchase, rent, or deploy to keep everything running without skyrocketing costs, unbearable latency, or performance bottlenecks due to unexpected issues.
In that arena, three names recur in nearly any serious project: NVIDIA A100, NVIDIA H100, and NVIDIA H200. At first glance, they seem to be a logical ladder of power. In practice, the choice is more complex: it’s not always the “newest” GPU that wins, because what matters isn’t marketing but the type of workload (training, inference, model size, long context, parallelism, etc.).
What shifts the game is that these GPUs aren’t only differentiated by “being faster.” In the real world, performance is determined by three factors that often compete with each other:
- Compute: how much “raw power” they have for matrix multiplications.
- Memory: VRAM capacity and, especially, bandwidth.
- Interconnection: how well they scale when multiple GPUs are required beyond a single unit.
The key point: the bottleneck isn’t always where you think
To understand why A100, H100, and H200 can perform so differently, let’s translate it into everyday language:
- If the workload is limited by compute, the GPU is like a kitchen: more “burners” and a better “engine” cook faster.
- If the workload is limited by memory, the GPU is like a warehouse and loading dock: it doesn’t matter how large the kitchen is if ingredients arrive late or don’t fit.
In large models (LLMs), especially during inference, the system may spend more time moving weights and activations from memory than “computing” itself. That’s why two GPUs with similar compute capabilities can perform very differently if one has more bandwidth or significantly larger VRAM.
Specifications that matter (without getting lost in the technical specs)
There’s a comparison that sums up the generational leap well: A100 is a solid benchmark, H100 drastically boosts performance and adds new capabilities, and H200 keeps H100’s foundation but pushes memory power even further.
Summary (typical server platform values):
| GPU | Memory | Type of Memory | Approx. Bandwidth | NVLink (approx.) |
|---|---|---|---|---|
| NVIDIA A100 | 80 GB | HBM2e | 2.0 TB/s | 600 GB/s |
| NVIDIA H100 | 80 GB | HBM3 | 3.35 TB/s | 900 GB/s |
| NVIDIA H200 | 141 GB | HBM3e | 4.8 TB/s | 900 GB/s |
These numbers aren’t just for show; they explain why a GPU might be more than enough for an 8B parameter model but struggle with a 70B model that requires long contexts, high concurrency, or a large KV cache.
A100: The veteran that still “stands up” (if you don’t ask for miracles)
The A100 has been the workhorse of AI for years for a simple reason: balance. In many inference and mid-training scenarios, it remains perfectly valid, especially if the model comfortably fits in VRAM and doesn’t demand extreme bandwidth.
But the world has changed: current LLMs and their deployment (RAG, long contexts, agents, large batches, low latency) tend to expose memory and bandwidth limitations. In those cases, A100 doesn’t “fail” but leaves some performance on the table.
H100: the leap that’s not just speed, it’s “a different way to run”
H100 isn’t just “A100 but faster.” Its appeal lies in being designed to maximize modern workloads, especially transformers, and it features a key element that makes a difference in real environments: FP8 and its ecosystem.
Simply put: FP8 reduces data movement costs and boosts performance in certain scenarios, but it’s not magic. It requires software and workflows optimized to leverage it properly, and not every project can (or wants to) change precision, calibrate, quantize, or accept trade-offs.
For teams that can utilize it, H100 generally hits the “sweet spot”: high performance, broad availability in infrastructure, and a clear upgrade over A100.
H200: the “H100 with steroids” in memory (and that phrase explains almost everything)
Here’s a trap many overlook: H200 isn’t a radical architectural change from H100; the big difference is memory: more capacity and higher bandwidth.
How does this show up?
- Large models that, due to VRAM limits, would require more GPUs with H100.
- Long contexts (16K, 32K, or more) where the KV cache grows and consumes memory.
- Higher concurrency without a big hit on latency.
- Less complexity: needing fewer GPUs for the same work reduces synchronization, communication, and failure points.
In short: H200 shines when the bottleneck isn’t “computing” but fitting and moving data.
The tough question: When should you pay for H200 and when stick with H100?
In real-world terms, the decision often becomes clear if you honestly answer three questions:
- Does your model fit “well” in 80 GB with room for KV cache and activations?
If yes, H100 is usually the more rational choice. - Will you serve long contexts or workloads with high concurrency?
If yes, H200 starts to make sense. - Does your deployment require many GPUs purely for memory (not compute)?
If yes, H200 could be more cost-effective overall because it simplifies parallelism and reduces the number of GPUs needed.
This last point explains why, in some projects, the decision isn’t “H200 is expensive,” but rather “H200 prevents having to use twice as many H100s.”
The deeper insight: AI is pushing hardware toward a new limit
This debate isn’t just about engineering preferences. It’s a sign of the times: AI is driving infrastructure into an era where the “best chip” alone isn’t enough. Memory, power consumption, cooling, availability, and operational costs are just as critical.
That’s why comparing A100, H100, and H200 isn’t as simple as a ranking. It’s more useful to recognize a harder truth: choose the GPU that targets your bottleneck, not the one with the newest name.
Frequently Asked Questions
Which GPU is best for inference with long contexts (16K or more)?
When contexts grow, the KV cache consumes a lot of VRAM. In these cases, H200 often has an advantage due to its 141 GB and higher bandwidth, reducing the risk of running out of memory or needing to reduce concurrency.
What’s the practical difference between HBM3 and HBM3e in AI?
Beyond the “name,” HBM3e typically offers more bandwidth and, depending on configuration, more capacity. This is especially noticeable in inference of large models, where data movement from memory influences tokens per second.
What does FP8 mean, and why is it so closely associated with H100/H200?
FP8 is a lower-precision format that can improve performance and efficiency in compatible workloads. H100 and H200 utilize specialized software (like Transformer Engine) to make FP8 viable in real scenarios.
Is the A100 still a good choice in 2026?
Yes, if the model and use case aren’t dominated by memory or bandwidth constraints. For moderate model inference or workloads where cost is key and performance is sufficient, the A100 can still fit well.