The Best GPU for LLM Inference | Three Key Factors

There's no single "best GPU" for LLM inference. The right choice depends on three factors: memory capacity, memory bandwidth, and hourly cost. Get these three right, and your GPU selection practically makes itself.

This guide breaks down each factor, shows you how it affects your project, and helps you match the right GPU to your actual workload. It focuses on the most common data center GPUs used in production LLM inference today: the H100, H200, A100, and L4. It's not a full market survey. It's a practical decision framework for teams deploying inference at scale.

Factor #1: Memory Capacity: Determines Your Model Size Limit

If the GPU doesn't have enough memory to hold your model, nothing else matters. Your project stalls before it starts.

Think of GPU memory like desk space. Your model's parameters go on the desk. The temporary data generated during each conversation goes on the desk too. If the desk is too small, the work simply can't happen.

Take a 70B-parameter model as an example. Compressed to FP8 precision (a widely used optimization format that speeds up inference with minimal quality loss), the model weights alone take up roughly 35GB. But that's just the model itself. During inference, the GPU also needs room for context memory, the data that lets the model "remember" the current conversation. Add those together, and 80GB of memory is usable but tight on headroom.

If you're running larger models or processing long text inputs, 80GB won't cut it. The H200's 141GB gives you the breathing room you need. On the other hand, if you're experimenting with smaller models around 7B parameters, the L4's 24GB is usually sufficient.

How to choose? Start by calculating how much memory your model actually needs, then match it to the right card:

• H100 SXM — 80 GB
• H200 SXM — 141 GB
• A100 80GB — 80 GB
• L4 — 24 GB

Sources: NVIDIA H100 Tensor Core GPU Datasheet (2023); NVIDIA H200 Tensor Core GPU Product Brief (2024); NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA L4 Tensor Core GPU Datasheet.

For mainstream models (70B and under), 80GB works. For larger models or long-context workloads, go with 141GB. For lightweight experiments, 24GB will do.

But fitting the model is just the starting line. The next question is: how fast can it actually run?

Factor #2: Memory Bandwidth: Determines Response Speed and Concurrency

If your GPU can't read data fast enough, users sit there waiting for every response. Latency goes up, concurrency goes down, and your server bills go up with it.

Here's how LLM text generation works: every time the model outputs a token, the GPU has to read through the model data stored in memory. That means memory bandwidth, how fast the GPU can read from its own memory, sets the ceiling on how fast text comes out. Faster reads, faster replies. Slower reads, users wait.

The gap between GPUs is massive:

• H100 SXM — 3.35 TB/s
• H200 SXM — 4.8 TB/s
• A100 80GB — 2.0 TB/s
• L4 — 300 GB/s

Sources: NVIDIA H100 Tensor Core GPU Datasheet (2023); NVIDIA H200 Tensor Core GPU Product Brief (2024); NVIDIA A100 Tensor Core GPU Datasheet; NVIDIA L4 Tensor Core GPU Datasheet.

That's over a 10x difference between the fastest and slowest.

In NVIDIA's official benchmarks, the H200 delivered roughly 1.9x faster inference than the H100 on Llama 2 70B. Most of that gain comes directly from higher memory bandwidth. The improvement is especially pronounced for long-text generation.

(Test conditions: NVIDIA TensorRT-LLM inference engine, FP8 precision, 64 concurrent requests, 128 input tokens / 2,048 output tokens per request. Source: NVIDIA H200 Product Brief, 2024.)

How to choose? For latency-sensitive production workloads with heavy concurrency, prioritize high-bandwidth cards like the H200 or H100. The A100 works for moderate loads where speed isn't mission-critical. The L4 is better suited for development and testing.

But speed alone doesn't close the deal. A blazing-fast card that blows your budget isn't a win either.

Factor #3: Hourly Cost: Determines Overall Value

Pick the wrong card and you lose either way: overspend on hardware you don't need, or underspend and waste time redeploying when performance falls short.

GPU cloud services charge by the hour. As a reference, H100 instances run around $2.10/hour and H200 instances around $2.50/hour (check gmicloud.ai/pricing for current rates).

That's only $0.40/hour apart. But the real question isn't "how much more does it cost?" It's "what do I get for the extra money?"

When is paying more worth it?

If you're running 70B+ models, or your application processes long texts (think document summarization, extended multi-turn conversations), the H200's extra 61GB of memory and 1.4x bandwidth advantage translate directly into faster responses and higher concurrency. In these scenarios, the extra $0.40/hour buys meaningful improvements in user experience and server utilization.

When is paying more a waste?

If you're only running 7B–13B models with moderate traffic, the H100's 80GB and 3.35 TB/s bandwidth are more than enough. Upgrading to an H200 would be like renting a warehouse to store a single desk. The extra capacity just sits there unused. In fact, an A100 or even an L4 might be all you need.

What about consumer GPUs like the RTX 4090 or 5090? They can technically run inference, but NVIDIA's GeForce EULA restricts data center use (see nvidia.com/en-us/drivers/geforce-license). Using them in production carries compliance risk. It's not worth it.

Now that you've got all three factors, let's map them to actual GPU choices.

Common Production LLM Inference GPUs by Use Case

Here's the full comparison at a glance:

• H100 SXM — Memory: 80 GB. Bandwidth: 3.35 TB/s. Reference price: ~$2.10/hr.
• H200 SXM — Memory: 141 GB. Bandwidth: 4.8 TB/s. Reference price: ~$2.50/hr.
• A100 80GB — Memory: 80 GB. Bandwidth: 2.0 TB/s. Reference price: contact for rates.
• L4 — Memory: 24 GB. Bandwidth: 300 GB/s. Reference price: contact for rates.

Memory and bandwidth sources: NVIDIA H100 Datasheet (2023); NVIDIA H200 Product Brief (2024); NVIDIA A100 Datasheet; NVIDIA L4 Datasheet. Pricing source: GMI Cloud; check gmicloud.ai/pricing for current rates.

Now match your scenario:

• Online chatbot or Q&A on 7B–70B models, low latency, growing user base → H100 SXM. 80GB fits mainstream models; 3.35 TB/s bandwidth handles low-latency, medium-to-high concurrency.
• 70B+ models, long-document summarization, extended conversations → H200 SXM. 141GB memory holds large models plus long context; 4.8 TB/s bandwidth keeps long-text generation smooth.
• Smaller models (7B–34B), budget is the main constraint, latency requirements aren't extreme → A100 80GB. 80GB is enough; lower hourly cost suits stable workloads that aren't speed-critical.
• Validating an idea, running 7B models for experiments or prototyping → L4. 24GB handles small models; lowest cost, ideal for teams still in the testing phase.

Recommendations based on the three-factor framework above: memory capacity, bandwidth, and cost.

When You Don't Need an Expensive Card

This is worth spelling out, because a lot of teams overspend here:

You don't need an H100 for 7B model experiments. The L4's 24GB is more than enough for 7B inference. It costs a fraction of the price and lets you validate your idea before committing to bigger hardware.

H200 advantages are minimal when concurrency is low and context is short. If your user base is still small and your texts aren't long, the H200's extra memory and bandwidth mostly go unused. Start with an H100 or even an A100, then upgrade when your workload genuinely demands it.

When budget is tight, A100 and L4 are smarter starting points. Not every project needs top-tier hardware on day one. Use hardware that's good enough to prove the business case, then scale up based on real traffic data.

We Hope This Guide Helps

We hope this article makes your next GPU decision a little easier. If you still have questions or want to dig deeper, visit GMI Cloud (gmicloud.ai) to find the answers you're looking for.

FAQ

Q: Can I use an RTX 4090 for production inference?

Technically yes, but it's not recommended. NVIDIA's GeForce EULA restricts data center deployment. Fine for development and testing. Risky for production.

Q: When is the H200 worth the extra cost over the H100?

Two scenarios: first, when your model exceeds 80GB of memory (say, a 70B model with long context, or 100B+ models); second, when your application is highly latency-sensitive with heavy concurrency. The H200's extra 61GB of memory and 1.4x bandwidth advantage deliver the clearest ROI in those cases. If neither applies, the H100 or A100 will serve you fine.

Q: What if I don't need a dedicated GPU at all?

If what you need is access to model capabilities rather than managing your own GPU infrastructure, consider using an API service. Many cloud platforms offer pay-per-request inference endpoints. No hardware management, faster time to first result.

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