Question 1

How much VRAM do I need to run a 70B model?

Accepted Answer

A 70B model at Q4 quantization typically requires around 35-40 GB of VRAM. You can run it on a multi-GPU setup, a Mac with 48GB+ unified memory, or a cloud GPU instance.

Question 2

Can I run LLMs on a consumer GPU?

Accepted Answer

Yes! Many models like Llama 3.2 3B, Gemma 2 2B, and Phi-3 Mini can run on GPUs with 6-8 GB VRAM using 4-bit quantization. Use LocalOps to find the best match for your hardware.

Question 3

What is quantization and does it affect quality?

Accepted Answer

Quantization reduces model precision (e.g., from FP16 to Q4) to lower VRAM usage. Q4_K_M is the recommended sweet spot, offering ~50% VRAM savings with minimal quality loss.

Format	VRAM Need	Tier
FP16	144.0 GB	Full Precision
Q8_0	72.0 GB	High
Q6_K	61.2 GB	Excellent
Q5_K_M	50.4 GB	Great
Q4_K_M	36.0 GB	Sweet Spot
Q2_K	21.6 GB	Emergency

NuminaMath 72B

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Quantization Estimates

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