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	200.0 GB	Full Precision
Q8_0	100.0 GB	High
Q6_K	85.0 GB	Excellent
Q5_K_M	70.0 GB	Great
Q4_K_M	50.0 GB	Sweet Spot
Q2_K	30.0 GB	Emergency

nanowhale

Specifications

Build your Local Rig

Instant Cloud GPUs

Quantization Estimates

Share this Model

Similar Models

Related Guides

VRAM Deep Dive

GPU Buyer Guide