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	796.0 GB	Full Precision
Q8_0	398.0 GB	High
Q6_K	338.3 GB	Excellent
Q5_K_M	278.6 GB	Great
Q4_K_M	199.0 GB	Sweet Spot
Q2_K	119.4 GB	Emergency

Jamba 1.5 Large

Specifications

Run in the Cloud

Instant Cloud GPUs

Quantization Estimates

Share this Model

Similar Models

Jamba 1.5 Mini

Jamba Mini

Jamba v0.1

Related Guides

VRAM Deep Dive

GPU Buyer Guide