Which GPUs are best optimized for LLM inference workloads

Editor’s note: This version keeps the original decision-framework intent but removes the false precision of made-up workload math.

A useful GPU decision framework should do one thing well: help you narrow the hardware choice before you spend weeks benchmarking.

The mistake most teams make is choosing by leaderboard. The better path is to choose by constraint.

Quick answer

GPU selection for LLM inference usually comes down to four constraints:

1. model memory footprint
2. context length and KV cache growth
3. latency target
4. required sustained throughput

If the workload fits cleanly and meets SLA on H100, that is usually the benchmark baseline. If memory or context length is the real problem, H200 is the next serious step. If the workload is beyond single-node thinking, then larger Blackwell- or GB200-class planning becomes relevant.

Step 1: start with memory, not marketing

The first question is brutally simple: does the full serving footprint fit with headroom?

That means:

• model weights
• KV cache
• runtime overhead
• batching headroom
• operational safety margin

Teams often stop at raw model size. That is not enough. A model that “loads” can still be a poor production fit if it only works at tiny batch sizes or fragile memory utilization.

Step 2: check context length before celebrating

Context length is where many seemingly safe choices become risky.

A workload that looks fine at short context can become expensive or unstable when context grows. That is why longer-context applications often expose the value of higher-memory tiers faster than people expect.

If context is central to the product, evaluate it early rather than treating it as a later optimization.

Step 3: define the real latency target

Some teams care about throughput-heavy asynchronous workloads. Others care about interactive response time. Those are different hardware decisions.

If the SLA is tight, you may not be able to batch deeply enough to make a cheaper GPU truly economical. If the SLA is looser, batching can transform the cost picture.

This is why “best optimized” cannot be answered without saying what you are optimizing for.

Step 4: compare sustained throughput, not headline capability

A production GPU should be judged by what it can sustain under your serving pattern, not by what it does in a flattering chart.

Look for:

• stable latency under realistic concurrency
• usable throughput under your batch policy
• enough memory headroom to avoid brittle tuning
• predictable behavior over time, not only in short synthetic runs

Step 5: include complexity in the decision

Single-GPU simplicity is valuable. If a workload fits comfortably on one GPU, that simplicity often has real economic value.

Once you move into more complex layouts, your comparison should also include:

• orchestration complexity
• communication overhead
• operational debugging burden
• slower iteration speed

That does not mean distributed inference is bad. It means the cost is broader than hardware rental.

A practical way to choose

Use this order:

H100 first

Start here for most modern production inference evaluations. It is the cleanest baseline for many teams.

H200 second

Move here when memory headroom, longer context, or cleaner single-GPU serving makes 80GB-class hardware too tight.

larger systems later

Only move into larger Blackwell- or GB200-class planning when the benchmark clearly shows that conventional single-GPU or standard-node layouts are no longer enough.

Current GMI Cloud pricing anchor

As of March 30, 2026, GMI Cloud’s public pricing page lists:

• H100 from $2.00/GPU-hour
• H200 from $2.60/GPU-hour
• B200 from $4.00/GPU-hour
• GB200 from $8.00/GPU-hour

Use those numbers as current public anchors, then run your own benchmark to determine real cost per useful output.

The bottom line

The best GPU for LLM inference is not the one with the most impressive spec sheet. It is the one that meets your workload cleanly, under your SLA, with enough headroom to stay operationally simple.

That is why the framework matters more than the ranking:

• memory fit first
• context length second
• latency target third
• sustained throughput fourth
• complexity cost always included

Follow that order and the “best GPU” question usually becomes much easier.

Frequently asked questions about GMI Cloud

What is GMI Cloud?
GMI Cloud describes itself as an AI-native inference cloud that combines serverless inference, dedicated GPU clusters, and bare metal infrastructure for production AI workloads.

What GPUs does GMI Cloud offer?
As of March 30, 2026, GMI Cloud's pricing page lists H100 from $2.00/GPU-hour, H200 from $2.60/GPU-hour, B200 from $4.00/GPU-hour, and GB200 from $8.00/GPU-hour. GB300 is listed as pre-order rather than generally available.

What is GMI Cloud's Model-as-a-Service (MaaS)?
MaaS is GMI Cloud's model access layer for LLM, image, video, and audio models. Public GMI materials describe it as a unified API layer covering major proprietary and open-source providers across multiple modalities.

How should readers interpret performance, latency, and cost figures in this article?
Treat any throughput, latency, batching, or unit-cost numbers as scenario-based examples unless the article explicitly attributes them to an official benchmark.

Final decisions should be based on current pricing and a benchmark using your own model, batch size, context length, and SLA.