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Can I run Phi-4 14B on Nvidia GeForce RTX 2060 (6GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Phi-4 14B needs ~10.8 GB even at Q4_K_M, but Nvidia GeForce RTX 2060 (6GB) only has ~5 GB usable.

Needs ~10.8 GB Device usable ~5 GB

Needs ~10.8 GB even at Q4_K_M, but only ~5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 5.8 GB: Phi-4 14B needs roughly 10.8 GB at Q4_K_M and Nvidia GeForce RTX 2060 (6GB) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Phi-4 14B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Phi-4 14B on Nvidia GeForce RTX 3060 (12GB).

Too big for Nvidia GeForce RTX 2060 (6GB)'s VRAM, but you could offload some layers to system RAM and still run it at roughly ~6 tok/s, far slower than a model that fits. How this is estimated.

Q4_K_M needed
~10.8 GB
Usable on device
~5 GB
Device memory
6 GB
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Which quant fits

Quant ladder vs ~5 GB usable
Q2_K
~7.6 GB
Q3_K_M
~8.5 GB
Q4_K_M
~10.8 GB
Q5_K_M
~11.7 GB
Q6_K
~13.2 GB
Q8_0
~17.3 GB
FP16
~29.7 GB
The line marks Nvidia GeForce RTX 2060 (6GB)'s ~5 GB budget; rungs past it are too large.
Model phi
Parameters
14B
Q4_K_M size
9.05 GB
Q8_0 size
15.58 GB
Context
16k
Ollama tag
phi4:14b
Full Phi-4 14B requirements →
Device Windows
Memory
6 GB vram
Usable for weights
~5 GB
Power draw
~160 W
Best runtime
Ollama (CUDA) / llama.cpp CUDA
Best models for Nvidia GeForce RTX 2060 (6GB) →

What you can run instead

Run Phi-4 14B on other hardware

FAQ

Can Nvidia GeForce RTX 2060 (6GB) run Phi-4 14B?

No. Phi-4 14B needs ~10.8 GB even at Q4_K_M, but Nvidia GeForce RTX 2060 (6GB) only has ~5 GB usable.

How much memory does Phi-4 14B need?

Nvidia GeForce RTX 2060 (6GB) does not have enough memory. At Q4_K_M the weights are ~9.05 GB; with KV cache and runtime overhead, budget ~10.8 GB at a 4k context.

What is the best tool to run Phi-4 14B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.