How much VRAM do I need for Foundation-Sec-8B-Instruct-GGUF Q5_K?

Yes! At 7.63 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 Foundation-Sec-8B-Instruct-GGUF?

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 Q5_K quality good enough for production?

Q5_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for Foundation-Sec-8B-Instruct-GGUF Q5_K

Recommended GPU Configurations

RTX 3080 (10GB)

RTX 4070 Ti (12GB)

RTX 4090 (24GB)

📊 VRAM Calculation Breakdown

Try a Different Quantization

Foundation-Sec-8B-Instruct-GGUF — Other Quantizations

Frequently Asked Questions

VRAM Requirements for
Foundation-Sec-8B-Instruct-GGUF Q5_K