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FAQ

AI hardware: FAQ

What is the RTX 4090 48GB?

The RTX 4090 48GB is a factory NVIDIA RTX 4090 board reworked with double the standard VRAM. It is not an NVIDIA product line: the base card is NVIDIA’s, the memory rework is not. Its specification is otherwise identical to a 24GB RTX 4090 (same 16,384 CUDA cores, same 2520 MHz boost clock, same 384-bit bus), but with 48GB of GDDR6X and 1.01 TB/s of memory bandwidth.

Why buy a VRAM-modded GPU instead of a server-grade card?

Cost and speed, without giving up reliability. A modded card is a factory NVIDIA board reworked with higher-capacity memory of the same grade, then burned in under sustained load before it ships, so it behaves like the card it started as. It runs on stock NVIDIA drivers and stock CUDA on both Windows and Linux, with no patched runtime, so nothing in your stack has to change to use it. It presents as a 4090 with 48GB of VRAM, which is exactly what you want your framework’s allocator to see. What changes is the price and the throughput: 48GB of VRAM costs a fraction of a datacentre card carrying the same memory, and in sustained deep learning work the RTX 4090 48GB outruns an RTX 6000 Ada, whose 300 W power limit stops it reaching its advertised numbers.

Does the RTX 4090 48GB need special drivers or a patched CUDA?

No. It runs on stock NVIDIA drivers with stock CUDA, on both Windows and Linux, and works with any NVIDIA software out of the box exactly as a standard RTX 4090 does. There are no additional installation steps and no restricted operating system support.

Is the RTX 4090 48GB faster than the RTX 6000 Ada?

In real deep learning workloads, yes. On paper the RTX 6000 Ada has more CUDA and Tensor cores, but its 300 W power limit prevents it from reaching its advertised throughput in sustained work. In our benchmarking the RTX 4090 48GB was 11.6% faster on Llama 2 13B token generation and 20 to 25% faster on FLUX.1 image generation.

I also see an RTX 4090D 48GB, what is the difference?

The RTX 4090D 48GB is a rework of the China-only RTX 4090D board, which carries fewer Tensor cores for AI FLOPS and downgraded VRAM, lowering memory bandwidth and with it performance in bandwidth-bottlenecked workloads such as LLM inference. It also uses GDDR6 rather than GDDR6X, which is significantly slower again. That is why it looks cheaper. We supply both, and we recommend the full-powered version.

How much does an AI workstation cost?

Price depends on the GPU configuration, CPU platform, memory capacity and storage, and moves with GPU market pricing, so we quote per build rather than publishing a list price. Tell us your workload and budget and we will specify against it. We accept multiple payment currencies and methods.

Why air cooling rather than a liquid AIO cooler?

Liquid all-in-one coolers carry a leak risk directly above the most expensive components in the machine, and deep learning does not need CPU overclocking. A well-chosen air cooler with high-airflow case fans handles sustained load quietly and has nothing to fail wet.

What do you preinstall on the workstation?

Ubuntu with the CUDA Toolkit installed and validated against the GPUs, so the machine trains on arrival rather than after a week of driver debugging. We can match a specific Ubuntu and CUDA version to your existing environment on request.

Can you specify a build for our specific workload?

Yes, and we prefer to. Image-based training pipelines are usually CPU-bottlenecked in preprocessing and benefit from single-core speed and memory bandwidth; large-model inference is bound by VRAM and memory bandwidth. Tell us what you actually run and the specification follows from that.

Do you supply hardware for on-premise LLM deployment?

Yes. Where a corpus cannot leave your network, we build the workstation or server and deploy open-weight models onto it, so inference runs entirely inside your own infrastructure. See our LLM and agentic AI solutions for the software side.