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
Model size
First input

Parameter count and quantization set the VRAM floor.

Traffic pattern
Second input

Single-user tests and production concurrency are different problems.

Start with fit
Best shortcut

If the model cannot fit, every other optimization is irrelevant.

Working details

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 exact GPU picks for every model, Jungle Grid lets the operator submit workload intent and score the live pool at dispatch time. That is a more durable operating model for teams with multiple models or providers.

Next step

Move from the guide into a real route decision

If this guide answered the concept, the next move is to test a route, price a workload, or jump into model-specific pages for concrete deployment numbers.

Try Jungle GridBrowse all guides
PricingGPU pricing and cost estimatorCheck a live workload estimate instead of stopping at theory.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.

Why does this page need model links?

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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