A team spins up LLM serving on the GPU they can grab the fastest. The first days look great, but after real traffic shows up, your memory fills up way faster than you’d expect, your latency gets all uneven, and keeping the system stable becomes your main job.

From our side of the stack, GPU choice isn’t usually about the highest benchmark scores. What really matters is whether the model weights and KV cache fit without constant tuning, how the system behaves under sustained load, and whether performance stays predictable when multiple requests collide. You’ll only see those details when you run inference workloads all the time, not when you do short-lived experiments.

This article looks at four GPUs we regularly see in real LLM deployments — RTX A6000, A100, H100, and B200 — and how each one tends to behave as workloads scale.

H100

The H100 is a great choice when your LLM is a paid feature or a core internal platform.

You’re paying for a very specific combo: lots of tokens per second, serious memory bandwidth, and scaling that behaves in real data-center deployments. The day-to-day payoff is simple: generation hiccups less, long-context runs stay snappier, and performance doesn’t fall off a cliff when you start stacking concurrent requests.

The cost is high: it’s hardware that’s expensive and often lives in expensive infrastructure. Power, cooling, rack space, and the rest of the stack are all part of the decision. But if your team is sick of fighting edge cases, H100 can help. It’ll cut down on those “weird nights” spent debugging instability.

A100

A100 is older, but it remains one of the most balanced cards for LLM hosting. The most practical advantage is that the ecosystem around it is well understood: drivers are stable, tooling is familiar, and there is a long track record of deploying it at scale. It also appears in the secondary market, which can make the cost-per-token attractive if you buy carefully.

A100 is also flexible. Multi-Instance GPU slicing can help when you run several smaller workloads or serve multiple models. That doesn’t solve capacity planning, but it can help keep utilization high without packing everything onto one giant model server.

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If you’re looking for a more predictable experience, a budget-friendly option, and a proven approach, A100 is often a good choice. For a lot of teams, this is also the phase where you want a stable “control plane” for the rest of the stack (API, DB, monitoring, queues) on a separate machine — a simple NVMe VPS is often enough for that part.

RTX A6000

Not every LLM deployment should be put in a data center. Some deployments just need a GPU that works in a normal server without turning into an infrastructure project. The RTX A6000 is built for that: workstation card, 48 GB VRAM, ECC, and plenty of room for serious inference in an ordinary chassis.

Its strengths show up when you’re working with smaller models or when you rely on quantization to keep memory use under control. You get enough headroom for batching without jumping straight to data-center hardware. It won’t compete with an H100 on raw throughput, and you won’t get the same enterprise tooling around it, but it can be reliable for stable, well-scoped workloads — internal assistants, regional deployments, or smaller production services.

If the realistic alternative is a consumer gaming GPU, the A6000 is often the less stressful option over time.

B200

B200 usually starts making sense when H100-class setups feel like the wrong kind of “overkill”: context windows keep expanding, models get heavier, and concurrency climbs — yet you still need stable, predictable output under load.

One of Blackwell’s practical advantages here is its low-precision formats. They can meaningfully raise throughput and improve efficiency, but only if your inference stack is actually built to take advantage of them — engine settings, quantization choices, and the right compute paths. Otherwise, you’re leaving performance on the table.

Also, this isn’t a “swap one GPU and call it a day” upgrade. B200 is typically deployed in larger, tightly integrated systems where fast interconnects, power delivery, and cooling are part of the deal.

If you’re trying to serve long-context workloads reliably while many requests run at once, B200 is designed for exactly that workload profile, and it’s the point where it usually makes sense to stop “building a GPU box” and instead run on production-grade GPU dedicated servers that are designed for this kind of load.

Industry context & 2026 outlook

Investors and strategists are taking a close look at the bigger picture of AI as we head into 2026. Some market pros are saying that the speed at which money is being invested — especially in data centers, GPUs, and related infrastructure — looks more like a tech bubble than steady growth.

Notably, investor Michael Burry, who gained fame for predicting the 2008 financial crisis, has publicly cautioned that speculative investment in AI stocks and infrastructure could form a bubble that’s hard to time or predict, even if AI fundamentals remain strong.

This doesn’t change the practical needs of production LLM hosting, but it does frame why long-term planning (balancing performance, cost, and real workload demands) matters now more than ever.