Microsoft Q8_0 14B Parameters

VRAM Requirements for
Phi-4 14B Q8_0

To run Phi-4 14B locally at Q8_0 quantization, you need at minimum 18 GB of GPU VRAM.

18 GB
Required VRAM
15.58 GB
File Size
16K tokens
Context Window
14B
Parameters
Estimated VRAM Required
18
GB
High-End Consumer GPU
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $600 – $900

Used RTX 3090 (24GB)

24 GB

Best used-market value for 24GB VRAM. Solid for 30B-class models.

Balanced $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Fastest 24GB consumer GPU. Excellent for daily local inference.

Ultimate $2,500 – $3,500

NVIDIA A5000 (32GB)

32 GB

Pro workstation card with ECC memory. Maximum headroom at 24GB.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 15.58 GB
Context Overhead (16,384 tokens × 14B × 2 ÷ 1M) 0.459 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 18 GB

Try a Different Quantization

Use the interactive calculator to compare Phi-4 14B across all available formats.

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Phi-4 14B — Other Quantizations

Q4_K_M 8.49 GB file
11 GB
Q8_0 Current
18 GB
FP16 29.5 GB file
32 GB
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Frequently Asked Questions

Can I run Phi-4 14B Q8_0 on a consumer GPU?
Yes! At 18 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Phi-4 14B?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.