Model requirements

LLaMA 3.1 8B GPU Requirements

LLaMA 3.1 8B usually starts around 6-8 GB in INT4, 10-12 GB in INT8, and 16-18 GB in FP16. A safe production starting point is RTX 4090 24GB or A10G 24GB.

Price LLaMA 3.1 8BEstimate cost
6-8 GB
INT4 start

Approximate starting range before runtime headroom.

16-18 GB
FP16 start

Useful for accuracy-first deployments.

RTX 4090 24GB or A10G 24GB
Safe GPU floor

A strong default when you want one safe answer fast.

VRAM table

LLaMA 3.1 8B memory and route profile

LLaMA 3.1 8B is primarily used for general chat and assistant inference. Most teams start with the quickest safe answer for memory fit, then compare which production routes make sense.

The ranges on this page are practical starting points for planning. Actual deployment requirements still depend on runtime overhead, batching, and the execution framework.

PrecisionApproximate VRAMTypical route
INT46-8 GBCheapest healthy route when quality holds
INT810-12 GBBalanced production starting point
FP1616-18 GBAccuracy-first route with more headroom

Execution notes

What changes the route in production

A memory-fit answer is only useful if the route is healthy. Pages like this should explain that fit, latency, and route quality all matter once the model goes live.

For LLaMA 3.1 8B, the most relevant follow-up pages are the cost page and the run-without-GPU page because those are the next practical questions most teams ask.

  • Chat endpoints
  • Agent backends
  • Low-friction production pilots

Next step

Take LLaMA 3.1 8B from research into a real route

Once the fit is clear, price the route and test one workload so you can compare the theory against live capacity.

Open the estimatorRun this workload
CostCost to run LLaMA 3.1 8BCheck the operating range and what changes the bill in production.DocsDocs and execution workflowInspect the API, CLI, and portal paths if you want to run the model immediately.

FAQ

Frequently asked

What GPU do I need for LLaMA 3.1 8B?

A safe starting answer is RTX 4090 24GB or A10G 24GB. Lighter quantized routes can use less memory, but that is the clean default most teams need first.

Can LLaMA 3.1 8B run on a consumer GPU?

In many cases yes, especially with quantization. The safer answer still depends on the exact precision, runtime overhead, and traffic shape you expect in production.

Why should this page link to pricing and run-without-GPU pages?

Because the next user question after requirements is usually either cost or whether the model can be run remotely without buying hardware directly.