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OP·Fredoline Eruo
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RUNLOCALAI · v38
Phi-4 Reasoning 14B / on / NVIDIA GeForce RTX 3080 16GB (Mobile)
Fits comfortably

Running Phi-4 Reasoning 14B on NVIDIA GeForce RTX 3080 16GB (Mobile)

NVIDIA GeForce RTX 3080 16GB (Mobile) runs Phi-4 Reasoning 14B comfortably at Q4_K_M with 5 GB of headroom for context.

By Fredoline Eruo·Latest benchmark evidence Jun 2, 2026

Model size

14B params
Phi-4 Reasoning 14B →

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 phi4-reasoning:14b
2. Run
ollama run phi4-reasoning:14b

Default quant in Ollama is Q4_K_M. To use a different quant, append it: phi4-reasoning:14b-q5_K_M.

Variants and what fits

QuantizationFile sizeVRAM requiredFits on NVIDIA GeForce RTX 3080 16GB (Mobile)?
Q4_K_M8.4 GB11 GB
Yes

Real benchmarks

ToolQuantContexttok/sVRAM usedDateEvidenceExport
—Q4_K_M4,09640.4 tok/s—Jun 2, 2026Measured here
operator: fred-oline
DetailSourceJSON

Frequently asked

Can NVIDIA GeForce RTX 3080 16GB (Mobile) run Phi-4 Reasoning 14B?

NVIDIA GeForce RTX 3080 16GB (Mobile) runs Phi-4 Reasoning 14B comfortably at Q4_K_M with 5 GB of headroom for context.

What quantization should I use?

Q4_K_M is the highest-quality variant of Phi-4 Reasoning 14B that fits in 16 GB VRAM. Lower-bit quants will be smaller but lose some quality.

How fast will it be?

Measured at 40.4 tok/s on this combination in our testing.

See also: Phi-4 Reasoning 14B, NVIDIA GeForce RTX 3080 16GB (Mobile), all benchmarks.

Reviewed by RunLocalAI Editorial. See our editorial policy.