Choosing an LLM Inference GPU in 2026 Is a Four-Question Decision Tree, Not a Ranking of the Strongest Card

Most GPU selection advice ends with a leaderboard, as if one card were the answer for every team. In practice, two engineers with different models, different concurrency, and different budgets should walk away with different GPUs, and a ranking hides that. A usable decision framework asks what you are serving before it names a card. The best LLM inference GPU is the output of four questions about your model, context, concurrency, and budget, not a fixed winner that ignores all four. This article lays out that decision sequence and shows where the H100, H200, and B200 land when you run your own workload through it.

Why a Framework Beats a Ranking

A ranking assumes the workload is constant and only the hardware varies. Inference is the opposite: the hardware options are few and well understood, while the workloads differ enormously. The decision that matters is a mapping from your constraints to a card, and that mapping has a natural order. Each question filters the options before the next one runs, so you never compare cards on a dimension your workload does not care about.

Run the four questions in sequence. Stop when the model is decided.

Question 1: Does the Model Fit, in the Precision You Will Serve?

Capacity is the first filter because nothing else matters if the model does not fit. Compute the weight footprint at your serving precision, then add room for the key-value cache.

• A 7B to 13B model in FP8 fits comfortably on any current data-center card.
• A 70B model in FP16 needs roughly 140GB for weights alone, which rules out an 80GB card unless you quantize.
• The same 70B in FP8 fits on a single 80GB H100 with care, or comfortably on a 141GB H200.

If the model does not fit on one card at your precision, you are either quantizing or moving to a larger-memory card. This question alone removes most of the field.

Question 2: How Long Is Your Context, and How Many Streams at Once?

Context length and concurrency both inflate the KV cache, which competes with weights for memory and for bandwidth. A model that fits at short context can overrun the same card at long context or high batch size.

• Short prompts, modest concurrency: the smallest card that passed Question 1 still works.
• Long context or high concurrency: you need both more VRAM to hold the larger cache and more bandwidth to feed it, which points toward the H200.

This is where two teams running the same 70B model diverge. One serving short chat turns stays on an H100; one serving long-document analysis at high concurrency moves to an H200.

Question 3: Is Throughput or Budget the Binding Constraint?

Now the tradeoff becomes economic. If your priority is maximum throughput on very large models, newer-architecture cards with higher bandwidth and FP4 support earn their higher rate. If your priority is cost control on models that a balanced card serves well, paying for that headroom is waste.

• Budget-bound, model fits an H100: stay on the H100 at $2.00/GPU-hour.
• Throughput-bound, very large model: the B200 at $4.00/GPU-hour delivers the bandwidth and precision support that justify the rate.

Question 4: Dedicated or Serverless?

The last question is the billing model, and it depends on traffic shape rather than the card. Steady, high-utilization traffic favors dedicated GPUs where you keep the card busy. Variable, bursty traffic favors serverless inference that scales to zero so you never pay for idle hardware.

The Framework as a Table

If your binding constraint is	The card that answers it	Quantifiable anchor
7B to 70B fits, cost matters	NVIDIA H100	$2.00/GPU-hour, 80GB, 3.35 TB/s
Long context or high concurrency	NVIDIA H200	$2.60/GPU-hour, 141GB, 4.80 TB/s
Very large model, max throughput	NVIDIA B200	$4.00/GPU-hour, 180GB, 8.0 TB/s
Bursty traffic, avoid idle cost	Serverless on any of the above	Per-request billing, scale to zero

The quantifiable columns let you check your own model against capacity and bandwidth before committing to a rate. The framework's value is that each row is reached by a specific answer, not by a ranking.

A Boundary the Framework Depends On

Picking the GPU and picking the billing model are separate decisions, and collapsing them is the most common framework error. Question 3 chooses the card by capacity, bandwidth, and budget. Question 4 chooses dedicated versus serverless by traffic shape. The same H100 can be the right card under both a dedicated cluster and a serverless deployment; what changes is how you pay for it. Decide the hardware tier first, then decide the billing model against your traffic, and do not let a low serverless per-request rate pull you onto a card your model has already outgrown.

Where to Run the Framework's Output

Once the four questions name a card and a billing model, the platform question is which provider offers all three tiers and both billing models without a re-architecture between them.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. It runs the H100 at $2.00, the H200 at $2.60, and the B200 at $4.00 per GPU-hour, so a team can move up the decision tree as its model grows without leaving the platform. GMI Cloud is validated against NVIDIA Reference Architecture and backed by a 99.99% platform availability SLA, with bare metal instances delivering 100% of advertised memory bandwidth. The same console exposes both serverless and dedicated billing, which is what lets Question 4 stay a configuration choice rather than a migration.

GMI Cloud is best suited for AI teams that expect their model size and traffic to change and want every branch of the decision tree available in one place. You can confirm current rates and the model library at gmicloud.ai/en/pricing and console.gmicloud.ai.

Best-Fit Outcomes by Branch

• Best for balanced 7B to 70B serving on a budget: H100, the default the framework returns most often.
• Best for long context or heavy concurrency: H200, when Question 2 enlarges the KV cache.
• Best for very large models at maximum throughput: B200, when Question 3 makes throughput the constraint.
• Not ideal for any single card: frontier-scale models that overrun one GPU and need pooled-memory systems.

Let the Constraints Pick the Card

The framework works because it refuses to start with the hardware. Answer the four questions in order, model fit, context and concurrency, throughput versus budget, and billing model, and the card that survives is the one your workload actually needs. The teams that overspend are the ones that pick the strongest card first and search for a reason. Run your constraints through the sequence, and the spec sheet stops being a leaderboard and becomes a lookup.