Local AI PC Configuration in 2026: Complete Guide and Comparison

Why run AI locally?

In 2026, artificial intelligence is no longer reserved for data centers. Thanks to new GPU architectures and optimized open-source models (Llama 4, Mistral, DeepSeek, Qwen…), it is now possible to run powerful LLMs directly on your own machine, without sending any data to the cloud.

For professionals subject to GDPR: lawyers, doctors, accountants, notaries, design offices: this is a revolution: you benefit from powerful AI, without ever exposing your client files to third parties.

• Total confidentiality: your data remains on your local network, no sending to external servers.
• Zero recurring subscription: once the machine is purchased, inference costs are zero.
• Low latency: the response is immediate, without depending on the quality of your connection.
• Offline operation: useful on site, on the go or in case of network failure.
• Complete customization: fine-tuning, RAG, agents: you control the entire environment.

Key criteria for an AI PC configuration

GPU VRAM: the #1 factor

For local AI, video memory (VRAM) directly determines the size of the models you can run. A 7 billion parameter LLM in 4-bit requires about 4-5 GB of VRAM; a 70B model in 4-bit requires 35-40 GB. The more VRAM, the larger models you can load or run multiple models simultaneously.

Good news: all our workstations are fully configurable, including the graphics card. The CoreAI 64, for example, offers consumer GPUs (RTX 5070 Ti to RTX 5090) but also professional GPUs up to 96 GB of VRAM (RTX 6000 Blackwell, L40S, H100…). If you have a specific need for GPU memory, you can build exactly the machine you need from the configurator, or contact us for a custom quote.

System RAM, processor, and storage

System RAM is used for complex pipelines (RAG, multi-agent orchestration, processing of large documents). 32 GB is a serious minimum; 64 GB and more become necessary as soon as you multiply parallel tasks. For the CPU, AMD's Ryzen 9 series in AM5 offer an excellent balance of computing power and memory bandwidth that AI frameworks need. Finally, a fast NVMe SSD accelerates the initial loading of model weights: count 1 TB minimum for comfortable use.

Comparison of the 3 Radiance Systems configurations

Radiance PC CoreAI 32: RTX 5070 Ti

The Radiance PC CoreAI 32 is the entry-level professional AI workstation. It is the ideal configuration for seriously starting with local AI, without breaking the bank, while maintaining a fully upgradeable machine.

Base configuration

• GPU: NVIDIA GeForce RTX 5070 Ti: 16 GB VRAM (Blackwell architecture)
• CPU: AMD Ryzen 9 9900X: 12 cores / 24 threads, AM5 socket
• RAM: 32 GB DDR5 5600 MHz: upgradeable up to 256 GB
• Storage: 1 TB NVMe (up to 3,500 MB/s)
• Power Supply: MSI 850W 80+ Gold PCIe 5
• OS: Windows 11 Professional (license included)
• WiFi 6E + Bluetooth included depending on the chosen motherboard

What you can run

With 16 GB of VRAM, the CoreAI 32 comfortably handles LLMs up to 13B in full quality, 7B models in long context, image generation with FLUX, Stable Diffusion XL, as well as light multimodal processing pipelines. This is the machine typically used by lawyers, SMEs, and freelancers who want efficient local AI without server constraints.

Radiance PC CoreAI 64: RTX 5090

The Radiance PC CoreAI 64 is the high-end AI workstation in the range. With the RTX 5090 and its 32 GB of VRAM, it can run current generation LLMs at full capacity, including 70B models with aggressive quantization. And if your needs go beyond consumer-grade, the configurator also offers professional GPUs: L40S 48 GB, RTX 5000 Blackwell 48 GB, RTX 6000 Blackwell 96 GB, or even H100 NVL for the most intensive workloads.

Base configuration

• GPU: NVIDIA GeForce RTX 5090: 32 GB VRAM (the most powerful consumer GPU in 2026)
• CPU: AMD Ryzen 9 9950X3D: 16 cores / 32 threads + 3D V-Cache
• RAM: 64 GB DDR5 6000 MHz: upgradeable up to 256 GB
• Storage: 1 TB NVMe (up to 3,500 MB/s): easily expandable
• Power Supply: Deepcool 1200W 80+ Gold
• OS: Windows 11 Professional (license included)
• WiFi 7 + Bluetooth included (MSI X870E)

What you can run

The RTX 5090 32 GB is the reference GPU for local AI on consumer workstations. It allows you to run Llama 4 Scout (109B MoE with 17B activation), Qwen 2.5 72B in 4-bit, distilled DeepSeek-R2, or heavy multimodal pipelines combining vision + text. It is also the reference configuration for light fine-tuning (LoRA, QLoRA) on enterprise datasets.

To go further, the configurator offers pro GPUs: the RTX 6000 Blackwell 96 GB can run 70B models in full precision or multiple models in parallel; the L40S 48 GB is optimized for server inference; and up to the H100 NVL 94 GB for the most demanding needs. VRAM is not a fixed ceiling: it adapts to your usage.

NVIDIA GB10 AI Mini Server: ASUS Ascent GX10

The ASUS Ascent GX10 (NVIDIA GB10 Grace Blackwell) is a category apart. It's not a classic workstation: it's a dedicated, ultra-compact (150×150×51 mm) AI mini-server, designed exclusively for AI workloads: local inference, fine-tuning, RAG, autonomous agents, multi-user deployment.

Unified architecture: the GB10 advantage

The NVIDIA GB10 chip combines ARM CPU (Grace, 20 cores) and Blackwell GPU in a 128 GB LPDDR5X unified memory architecture. Unlike a classic graphics card (separate VRAM), this unified memory is equally accessible by the CPU and GPU, without costly transfers between the two. The result: you can run 70B models in full precision or multiple models simultaneously, with exceptional memory bandwidth.

• 1 petaFLOP of AI power (INT8): for reference, the best gaming GPUs cap at ~0.35 PFLOPS INT8
• 128 GB of unified memory: running Llama 4 Maverick (400B MoE) is feasible
• Pre-installed DGX OS: CUDA, PyTorch, TensorFlow, Jupyter: everything is ready from startup
• Professional network connectivity: 10G LAN + ConnectX-7 (2×200G QSFP)
• Wi-Fi 7 + Bluetooth 5 integrated
• Ultra-compact desktop format: 1.48 kg, usable on any desk or in a rack

Who is it for?

The GB10 is aimed at organizations that want a dedicated AI server, separate from workstations: a medical practice that wants AI accessible to the entire team via the local network, a design office that deploys a RAG assistant on its internal documents, or an AI developer who wants a native Linux environment optimized for model training.

Configure your AI machine

Each configuration is fully customizable from our online configurator. All options are compatible and verified by our technicians before shipment.

Which configuration should you choose based on your usage?

The right choice depends on three factors: the size of the models you wish to use, your working environment (individual workstation vs. shared server), and your software stack (Windows or native Linux).

Frequently asked questions