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.
- INT4 start
- 6-8 GB
- Approximate starting range before runtime headroom.
- FP16 start
- 16-18 GB
- Useful for accuracy-first deployments.
- Safe GPU floor
- RTX 4090 24GB or A10G 24GB
- 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.
Use these ranges as practical planning estimates. Your actual requirements still depend on runtime overhead, batching, and the execution framework.
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.
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.
How can I estimate the cost of running LLaMA 3.1 8B remotely?
Use the pricing estimator after reviewing the requirements. It can help you compare a remote route with buying or managing hardware directly.