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OP·Fredoline Eruo
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RUNLOCALAI · v38
Llama 3.2 1B Instruct / on / NVIDIA GeForce RTX 3080 16GB (Mobile)
Fits comfortably

Running Llama 3.2 1B Instruct on NVIDIA GeForce RTX 3080 16GB (Mobile)

NVIDIA GeForce RTX 3080 16GB (Mobile) runs Llama 3.2 1B Instruct comfortably at Q4_K_M with 14 GB of headroom for context.

By Fredoline Eruo·Latest benchmark evidence Jun 2, 2026

Model size

1B params
Llama 3.2 1B Instruct →

Memory available

16 GB
NVIDIA GeForce RTX 3080 16GB (Mobile) →

Recommended quant

Q4_K_M
Highest quality that fits

Quick start with Ollama

1. Install
ollama pull llama3.2:1b
2. Run
ollama run llama3.2:1b

Default quant in Ollama is Q4_K_M. To use a different quant, append it: llama3.2:1b-q5_K_M.

Variants and what fits

QuantizationFile sizeVRAM requiredFits on NVIDIA GeForce RTX 3080 16GB (Mobile)?
Q4_K_M0.8 GB2 GB
Yes
Q8_01.3 GB2 GB
Yes

Real benchmarks

ToolQuantContexttok/sVRAM usedDateEvidenceExport
—Q4_K_M4,096189.5 tok/s—Jun 2, 2026Measured here
operator: fred-oline
DetailSourceJSON

Frequently asked

Can NVIDIA GeForce RTX 3080 16GB (Mobile) run Llama 3.2 1B Instruct?

NVIDIA GeForce RTX 3080 16GB (Mobile) runs Llama 3.2 1B Instruct comfortably at Q4_K_M with 14 GB of headroom for context.

What quantization should I use?

Q4_K_M is the highest-quality variant of Llama 3.2 1B Instruct that fits in 16 GB VRAM. Lower-bit quants will be smaller but lose some quality.

How fast will it be?

Measured at 189.5 tok/s on this combination in our testing.

See also: Llama 3.2 1B Instruct, NVIDIA GeForce RTX 3080 16GB (Mobile), all benchmarks.

Reviewed by RunLocalAI Editorial. See our editorial policy.