Actualbrain Q8_0 3.8B Parameters

VRAM Requirements for
Phi-3-mini-4k-CodeInstruct-GGUF_q8_0 Q8_0

To run Phi-3-mini-4k-CodeInstruct-GGUF_q8_0 locally at Q8_0 quantization, you need at minimum 6.1 GB of GPU VRAM.

6.1 GB
Required VRAM
4.04 GB
File Size
8K tokens
Context Window
3.8B
Parameters
Estimated VRAM Required
6.1
GB
Consumer Friendly
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 4.04 GB
Context Overhead (8,192 tokens × 3.8B × 2 ÷ 1M) 0.062 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 6.1 GB

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Phi-3-mini-4k-CodeInstruct-GGUF_q8_0 — Other Quantizations

Q8_0 Current
6.1 GB
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Frequently Asked Questions

Can I run Phi-3-mini-4k-CodeInstruct-GGUF_q8_0 Q8_0 on a consumer GPU?
Yes! At 6.1 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-3-mini-4k-CodeInstruct-GGUF_q8_0?
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.