We supply three very different NVIDIA systems for AI: the H200 8-GPU server, the RTX PRO 6000 Blackwell 8-GPU server and the compact NVIDIA DGX Spark. They are not three sizes of the same thing. Each one is built for a different job, and choosing the wrong one usually means paying for capacity you never use, or hitting a wall six months after go-live.
This guide explains what separates them, which numbers actually matter for AI, and how to match each system to your workload.
The short answer
- Training and fine-tuning large models: NVIDIA H200 8-GPU server.
- Running models in production for many users (inference): NVIDIA RTX PRO 6000 Blackwell 8-GPU server.
- Development, prototyping and local AI on one desk: NVIDIA DGX Spark.
The three systems at a glance
| NVIDIA DGX Spark | RTX PRO 6000 Blackwell 8-GPU server | NVIDIA H200 8-GPU server | |
|---|---|---|---|
| Form | Desktop unit, 150 × 150 × 50.5 mm | Rack server with 8 PCIe 5.0 GPUs | Rack server with 8 PCIe 5.0 GPUs |
| Memory | 128GB unified LPDDR5x, shared by CPU and GPU | 96GB GDDR7 ECC per GPU | 141GB HBM3e per GPU |
| Total memory | 128GB | 768GB | 1,128GB |
| Memory bandwidth | 273 GB/s | Up to 1.8 TB/s per GPU | 4.8 TB/s per GPU |
| Power | 240W external power supply | Up to 600W per GPU | Up to 600W per GPU |
| Best for | Developers, prototyping, local models up to 200B parameters | Inference, AI video, rendering and simulation | Training, fine-tuning and very large models |
Why GPU memory is the first number to check
For AI, the amount of GPU memory decides which models you can run at all. A useful rule of thumb is that a model’s weights need about 2 bytes per parameter at 16-bit precision, 1 byte at 8-bit (FP8) and roughly half a byte at 4-bit. So a 70-billion-parameter model needs around 140GB in 16-bit, about 70GB in FP8 and about 35GB in 4-bit, before you add memory for the conversation context.
Training needs far more. Full fine-tuning with a standard optimiser keeps gradients and optimiser states alongside the weights, which can add up to around 16 bytes per parameter. For a 70B model that is over a terabyte before activations, which is why serious training work needs the H200’s 1,128GB, or techniques such as LoRA that update only a small part of the model.
Why memory bandwidth decides speed
When a model generates text, each new token means reading the model’s weights from memory. That makes generation speed depend heavily on memory bandwidth. The H200’s 4.8 TB/s per GPU is roughly three times the RTX PRO 6000 and more than seventeen times the DGX Spark. Bandwidth is not the only factor, but it explains most of the difference in tokens per second for large models.
NVIDIA H200 8-GPU server: for training
Choose the H200 when you are training or fine-tuning models, running very large models at full precision, or working with long contexts. Each H200 carries 141GB of HBM3e, and NVLink bridges let GPUs exchange data much faster than over PCIe alone, which matters when a model is split across several GPUs.
Typical users are research teams, AI companies, banks and telecoms building their own models, and organisations in sectors such as pharmaceuticals and oil and gas that train models on their own data and cannot send it to a public cloud.
See the full specification of our NVIDIA H200 8-GPU server, with NVLink bridges, 3TB of memory and 8 × 200GbE networking.
RTX PRO 6000 Blackwell 8-GPU server: for inference
Most businesses spend far more GPU time running models than training them. The RTX PRO 6000 Blackwell is built for that. Each card has 96GB of GDDR7 memory, enough to hold a 70B model in FP8 on a single GPU, so an 8-GPU server can serve several models, or many users of one model, at the same time.
It is also a full graphics GPU with RT cores. The same server can handle rendering, simulation and AI video generation, which makes it a flexible choice for media, design and engineering teams as well as for AI customer service and document processing.
See the full specification of our RTX PRO 6000 Blackwell 8-GPU server.
NVIDIA DGX Spark: AI on your desk
DGX Spark puts NVIDIA’s GB10 Grace Blackwell Superchip in a box that fits in one hand. It has 128GB of unified memory and up to 1 petaFLOP of AI performance at FP4, and runs models of up to 200 billion parameters locally. Two units can be linked to work with models of up to 405 billion parameters.
It is the right tool for a developer or a small team that wants to prototype, fine-tune small models and test ideas without booking time on a shared server. It draws far less power than a rack server and needs no server room, but its 273 GB/s of memory bandwidth means it is not a replacement for a production inference server.
See NVIDIA DGX Spark specifications and availability.
Keeping your data on-premise
All three systems run in your own office, server room or an in-country data centre. For businesses in Egypt and the UAE that handle customer, financial or medical data, that means your models and your data stay under your control, with no per-token cloud bills.
Before you order: plan the room
An 8-GPU server is a serious electrical and cooling load. Eight GPUs at up to 600W each can draw up to 4.8kW on their own, before the rest of the server. Read our guide to power and cooling for 8-GPU AI servers before you choose where it will live.
How we can help
Our engineers size every GPU system to the models and users you actually plan to run, then build, burn-in test, deliver and install it. Answer three quick questions on our Enterprise Solutions page and we will reply within 24 working hours with a recommended configuration and price. If you are still deciding what to use AI for, start with our AI Solutions.
