RTX 3070 Ti 8GB runs Llama 3.1 8B Instruct — but there's less headroom than you'd think

Sized at Q4_K_M with a 8K context: weights, KV cache and framework overhead, against what RTX 3070 Ti 8GB leaves free. Llama 3.1 8B Instruct at Q4_K_M needs 6.7 GB and you have 8 GB available, so it fits with 1.3 GB to spare. Expect 38.4–73.7 tok/s (estimated).

What to do

Your machine already runs this

Llama 3.1 8B Instruct at Q4_K_M needs 6.7 GB and you have 8 GB available, so it fits with 1.3 GB to spare.

What we checked

VRAM required at Q4_K_M and 8K context: 6.7 GB
VRAM available on your hardware: 8 GB

Your machine is not exactly this one. Run this for your exact setup — the form opens pre-filled with RTX 3070 Ti 8GB and Llama 3.1 8B Instruct.

Questions people ask about this pairing

Can an RTX 3070 Ti 8GB run Llama 3.1 8B Instruct?

Yes. Llama 3.1 8B Instruct at Q4_K_M needs 6.7 GB and you have 8 GB available, so it fits with 1.3 GB to spare.

How fast will Llama 3.1 8B Instruct actually run?

Around 38.4–73.7 tok/s (estimated) on this setup at Q4_K_M. Decode speed on a local model is set by memory bandwidth rather than compute, so the figure moves with the card's bandwidth and the bytes read per token — not with its price.

Does a longer context change what Llama 3.1 8B Instruct needs here?

Yes, and it is the figure people forget. The 6.7 GB above already includes the KV cache at 8K; that cache grows roughly linearly with context, so doubling the window adds real gigabytes rather than a rounding error. If you plan to work with long documents on RTX 3070 Ti 8GB, size for the context you will actually use, not the default.

Data behind this page last checked 2026-09-29.