RTX 5060 8GB can run Gemma 3 12B Instruct, but not the way you'd expect. Here's the change that makes it work.

Gemma 3 12B Instruct at Q4_K_M needs 9.1 GB once weights, KV cache at 8K context and framework overhead are counted. RTX 5060 8GB offers 8 GB, leaving you 1.1 GB short. You are 1.1 GB short in VRAM but have 25.6 GB of usable system RAM, so llama.cpp can hold the overflow layers in RAM instead of refusing to load. Expect 11.7–24.2 tok/s (estimated).

What to do

Spill the extra layers into system RAM

You are 1.1 GB short in VRAM but have 25.6 GB of usable system RAM, so llama.cpp can hold the overflow layers in RAM instead of refusing to load.

What it needs

Use llama.cpp or Ollama, which support partial GPU offload
Keep at least 1.1 GB of system RAM free while the model is loaded

What we checked

Deficit: 1.1 GB
System RAM usable for offload: 25.6 GB
Offload throughput checked against the 5 tok/s usable floor

Other ways to get there

Run Gemma 3 4B Instruct instead — Gemma 3 4B Instruct is the same family at a smaller size and needs 3.6 GB at Q4_K_M, which your 8 GB already holds.
Replace your card with an RTX 4060 Ti 16GB — 16 GB holds Gemma 3 12B Instruct at Q4_K_M outright, closing the 1.1 GB you are short by.
Add an RTX 4060 Ti 16GB alongside your current card — Your 8 GB plus this card's 16 GB clears the 9.1 GB this model needs, without replacing what you already own.

Market conditions: New GPU prices are far above MSRP because the memory on the board costs several times what it did in 2025. Relief is not expected before 2027-Q4. Source, checked 2026-08-26.

Your machine is not exactly this one. Run this for your exact setup — the form opens pre-filled with RTX 5060 8GB and Gemma 3 12B Instruct.

Questions people ask about this pairing

Can an RTX 5060 8GB run Gemma 3 12B Instruct?

Not as it stands. Gemma 3 12B Instruct needs 9.1 GB at Q4_K_M and this machine has 8 GB available, a shortfall of 1.1 GB. You are 1.1 GB short in VRAM but have 25.6 GB of usable system RAM, so llama.cpp can hold the overflow layers in RAM instead of refusing to load.

Would more system RAM fix this?

Here, yes — up to a point. You are 1.1 GB short in VRAM, and 64 GB of system RAM gives llama.cpp somewhere to put the overflow layers without changing your graphics card. It works because the shortfall is small enough that the layers living in system RAM do not dominate each token.

Should I wait for prices to come down?

New GPU prices are far above MSRP because the memory on the board costs several times what it did in 2025. Relief is not expected before 2027-Q4. The current estimate for relief is 2027-Q4. If you can run this some other way meanwhile, waiting is defensible; if you cannot, the part still does the job today.

How fast will Gemma 3 12B Instruct actually run?

Around 11.7–24.2 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 Gemma 3 12B Instruct needs here?

Yes, and it is the figure people forget. The 9.1 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 5060 8GB, size for the context you will actually use, not the default.

Data behind this page last checked 2026-08-26.