NVIDIA Project DIGITS for 3000$, does it worth it? Link to heading
What is it? Link to heading
Recently NVIDIA has previewed a portable device capable of running LLM models, for 3000$ (release May 2025)
The device is optimized for on-premise inference of large language models (LLMs) using FP4 precision having 1 petaflop AI computing power.
Here’s a comparison of NVIDIA’s Project DIGITS with A100 graphics card, an industry standard for the last:
NVIDIA Project DIGITS 3000$
GB10 Grace Blackwell Superchip
1 petaflop AI computing performance at FP4 precision
128GB unified memory
Up to 4TB NVMe SSD storage
NVIDIA A100 GPU an industry standard for training large language models (LLMs). An industry standard nowadays for training AI models (inference requires less computational powers):
NVIDIA A100 GPU Starting at used $2000, new $7500+
40GB or 80GB memory
Up to 19.5 teraflops at FP32 precision
512GB system memory
7.68TB NVMe storage
But what the FP4 optimization means? Link to heading
4-bit quantization is a technique used in deep learning to reduce the memory and computational requirements of neural networks by representing weights and activations with 4 bits instead of the standard 32-bit floating-point format. However, this compression can lead to a loss in model accuracy, as the reduced precision may not capture the full complexity of the original data
But there are exclusions and some LLMs do not lose accuracy at quantization as stated in the research paper below:
we empirically investigate multiple LLMs featured on an open LLM leaderboard, discovering that the LLaMA3-70B model series have a unique accuracy degradation behavior with W8A8 per-channel post-training quantization. In contrast, other model series such as LLaMA2, LLaMA3-8B, Qwen, Mixtral, Mistral, Phi-3, and Falcon demonstrate robust performance with W8A8, sometimes surpassing their FP16 counterparts.
Source: https://arxiv.org/html/2408.15301v1
So the device is definitely worth the $ but only for the models which fit the memory limits and doesn’t degrade at quantization. The device’s is totally prepared for installation and allows to set up the system quickly without the need for specialized infrastructure which is another great benefit of it.