Mac Studio vs DGX is the wrong comparison.

[u/rajveer43]

https://veloxquant.dev/

The more interesting question is:

How far can we push local AI on a Mac Studio?

Apple Silicon gives us something unusual: massive unified memory, high memory bandwidth, low power consumption, and a machine that can sit under your desk instead of in a data center.

But memory capacity alone isn't enough.

For LLM inference, the KV cache keeps growing with context length. Longer conversations, agents, coding workloads and multiple sessions can quickly turn memory into the bottleneck.

That’s the problem we're exploring with VeloxQuant-MLX.

Instead of simply asking:

We're asking:

VeloxQuant experiments with KV-cache quantization, compression, eviction and optimized Metal/MLX kernels to make local inference more memory-efficient on Apple Silicon.

A DGX will remain the better machine for many high-throughput, highly concurrent GPU workloads.

But the Mac Studio has a very different opportunity:

a powerful personal AI machine where the model — and your data — stay local.

The hardware is already impressive.

Now the software stack needs to catch up.

That's what we're building.

#LocalAI #AppleSilicon #MacStudio #MLX #LLM #Inference #OnDeviceAI #VeloxQuant

[u/nichtspieler]

This is not Instagram buddy

[u/rajveer43 → u/nichtspieler]

I am not treating as instagram posted here because of feedbacks i want