Selection guide

How to Choose a GPU for LLM Inference

Choosing a GPU for LLM inference starts with the model, precision, concurrency target, and latency budget. Teams overspend when they shop by brand first and workload shape second.

Estimate your routeBrowse model pages
First input
Model size
Parameter count and quantization set the VRAM floor.
Second input
Traffic pattern
Single-user tests and production concurrency are different problems.
Best shortcut
Start with fit
If the model cannot fit, every other optimization is irrelevant.
Quick answer

Choose the smallest healthy route that fits your model and traffic target.

Start with the model's approximate VRAM requirement, then work backward from concurrency and latency goals. The goal is not the most powerful GPU; it is the lowest-friction route that fits and performs cleanly.

Start with the model's approximate VRAM requirement, then work backward from concurrency and latency goals. The goal is not the most powerful GPU; it is the lowest-friction route that fits and performs cleanly.

  • Fit first, then throughput, then cost.
  • Quantization often changes the answer more than vendor branding does.
  • Routing layers make this repeatable instead of per-model guesswork.

Practical guidance

The inputs that actually matter

Teams get cleaner decisions when they anchor on model size, precision, expected load, and latency target. Shopping by GPU family before those are clear leads to waste.

  • Model and precision
  • Expected request volume
  • Latency ceiling
  • Budget or cost target

Why static lookup charts are not enough

Static charts are useful for learning, but real deployment decisions depend on current healthy supply. The right route for a model this week may not be the right route tomorrow if the market changes.

How Jungle Grid changes the workflow

Instead of forcing an exact GPU pick for every model, Jungle Grid lets you submit the workload and handles placement against available capacity. That gives you one operating model across multiple workloads and providers.

Next step

Put this guidance to work

Put the guidance into practice: estimate a workload, check model requirements, or run your first job.

Try Jungle GridBrowse all guides
EstimatorGPU pricing and cost estimatorEstimate what your workload could cost before you run it.ModelsModel requirements and cost hubJump into model-specific GPU requirements, cost, and remote execution pages.DocsDocs and execution detailsInspect the API, CLI, and portal workflow if you want implementation detail next.

Related pages

Related pages to explore next

Use these pages to go deeper into pricing, model requirements, product details, and related comparisons.

Model pageLLaMA 3.1 8B GPU requirementsA concrete follow-up if you want one model-specific memory answer.Model pageMistral 7B GPU requirementsCompare a second compact model before you choose a route.LibraryModel requirements and cost hubBrowse the full model library by requirements, cost, and deployment pattern.

FAQ

Frequently asked

What is the biggest mistake in GPU selection for inference?

Choosing for peak safety without respecting actual workload shape. Teams then pay a premium for headroom they do not use.

Where can I find requirements for a specific model?

Because the user often wants the concrete follow-up immediately, such as the GPU requirements for LLaMA or Mistral rather than only the framework for thinking.

What should I do after reading this?

Use it to narrow the problem, then jump into a model page or pricing estimate when you want a concrete route and cost range.

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