A desktop built for local models needs more than a powerful graphics-card name. GIGABYTE AI TOP 100 B850 offers two very different graphics configurations, making software support and memory planning the first buying decisions. For a South African workstation, work backwards from the model and tools you need to run. A large power supply or a gaming benchmark cannot settle whether your chosen application will use one card, two cards or the available graphics memory effectively.
Quick Answer
The announced system uses Ryzen 9 9950X, 128GB DDR5 and a 2TB Gen4 SSD, with RTX 5090 or dual Radeon AI PRO R9700 graphics. The dual AMD configuration totals 64GB VRAM, subject to software support. No complete local system price is verified. A Palit RTX 5060 Dual 8GB was R7,999 in stock on 1 October 2026, a separate entry-level graphics reference.
🧠 Separate system memory from graphics memory
The 128GB DDR5 specification describes system RAM. It does not turn into 128GB of GPU memory when you launch a model. List the model, precision and application first, then establish the memory required by the way you intend to run it. Quantisation changes the workload, so a result for one numerical format should not become a promise for every other format.
Two Radeon AI PRO R9700 cards provide the stated combined 64GB graphics capacity. That is not automatically interchangeable with a single ordinary 64GB graphics card. Your software must support splitting or using the workload across the cards. Before placing a large order, ask whether the exact application supports the selected GPU configuration and whether its documentation describes the memory arrangement you need.

🖥️ Choose a configuration around the application
The September announcement specifies Ryzen 9 9950X with 16 cores and 32 threads. It also lists 128GB DDR5 and a 2TB Gen4 SSD. Those components form part of the complete system, so compare a full configuration rather than isolating the graphics card in a quote. Storage for models and datasets is another budget item; a headline capacity does not describe every user's collection.
GIGABYTE's performance examples use particular models and precision settings, including FP4. They are useful context for those tests, but they do not establish universal inference speed. Keep a separate list of the tasks that matter to you, such as an interactive model, batch processing or development. A workstation should be evaluated against those tasks and the software you can actually maintain.
🔌 Budget the complete workstation and workflow
The system page lists 240mm liquid cooling and a 1600W Platinum power supply. That is a power-supply specification, not a claim that the desktop always draws 1600W. Request the configuration's requirements and a complete rand quote before planning the desk. Cooling, support and software compatibility deserve attention alongside the purchase price.
Running a model locally also needs a clear data workflow. External APIs, network tools and third-party services can transmit information outside the computer. If offline operation matters, check each tool and connection instead of assuming the hardware alone provides privacy. A local model and an entirely local process are related decisions, but they are not identical.
Explore NVIDIA gaming PCs and processors for other configurations, while keeping their intended use separate from this announced workstation. The R7,999 GPU snapshot is a budget marker for a different 8GB card, not a substitute for the RTX 5090 or dual professional cards. Do not use its price or gaming role to predict this system's model capacity.
Frequently Asked Questions
Which processor is in the announced system?
The September announcement specifies Ryzen 9 9950X, with 16 cores and 32 threads. Confirm the complete configuration on any local quote.
What graphics configurations are announced?
One uses GeForce RTX 5090; the other uses dual Radeon AI PRO R9700 cards. Their software and memory requirements need separate assessment.
Is 128GB DDR5 the same as 128GB VRAM?
No. DDR5 system memory and graphics-card memory are different pools. A model requiring GPU memory cannot be sized from RAM alone.
Do two R9700 cards act like one ordinary 64GB card?
The stated combined capacity is 64GB. Software must support using multiple GPUs; do not assume automatic pooling for every model.
Are the advertised inference results universal?
No. They are internal tests with specified models and precision. Your software, quantisation and workload can produce different results.
Does local AI guarantee that data stays offline?
Only an entirely local workflow can do that. Network access, external APIs or third-party services can send data outside the computer.
Ready to plan a local AI desktop?
Size GPU memory and software first, then seek a complete rand quote without inventing local launch stock.