Acer Veriton AI Mini Workstation Personal Computer GN100-UD11 Series
Acer Veriton AI Mini Workstation Personal Computer GN100-UD11 Series
The Acer Veriton GN100 AI Mini Workstation brings server-grade AI compute to your desk instead of a data center. It is built around the NVIDIA GB10 Grace Blackwell Superchip, the same silicon at the heart of NVIDIA’s own DGX Spark platform, and delivers up to 1 petaFLOP of FP4 AI performance in a sealed chassis you can hold in one hand. It suits AI developers, researchers and businesses that need to run and fine-tune large models locally without cloud API costs, egress fees or data leaving the building.
Who it's for
- AI developers running and fine-tuning models with 200B+ parameters locally
- Legal, healthcare, financial services and R&D teams working with proprietary models, regulated data or client-confidential information
- Teams running NVIDIA NIM microservices locally, pre-configured through DGX OS
- Groups that have outgrown a single high-end GPU workstation but are not ready to commission a server room
Key features
- NVIDIA GB10 Grace Blackwell Superchip: a unified 20-core Arm CPU (10 Cortex-X925 performance cores + 10 Cortex-A725 efficiency cores) fused with a Blackwell-generation GPU carrying 6,144 CUDA cores and fifth-generation Tensor Cores.
- Up to 1 PFLOPS of FP4 AI performance (with sparsity): enough throughput to run and fine-tune models with 200B+ parameters locally.
- 128GB unified LPDDR5x memory at 273GB/s: shared between CPU and GPU, removing the CPU-GPU data transfer bottleneck that limits typical PC-plus-GPU setups.
- 4TB self-encrypting NVMe SSD: secure local storage for models, datasets and containers, backed by an integrated Kensington lock and a tamper-resistant 1.2kg sealed chassis.
- Dual 200Gbps NVIDIA ConnectX-7 ports: direct-attach a second GN100 for combined 405B-parameter inference, or link up to four units through a RoCE 200GbE switch for a small AI cluster.
- NVIDIA DGX OS pre-installed: a Linux-based platform tuned for the NVIDIA AI stack, with CUDA, PyTorch, NIM microservices and the NeMo Framework ready to run out of the box.
- Everyday connectivity: Wi-Fi 7, four USB 3.2 Type-C ports, HDMI 2.1b output and a dedicated Gigabit LAN port alongside the high-speed ConnectX-7 links.
Specifications
| Spec | Detail |
|---|---|
| Processor | NVIDIA GB10 Grace Blackwell Superchip, 20-core Arm CPU (10x Cortex-X925 + 10x Cortex-A725) |
| GPU | Blackwell-generation GPU, 6,144 CUDA cores, 5th-gen Tensor Cores |
| AI Performance | Up to 1 PFLOPS FP4 (with sparsity) |
| Memory | 128GB unified LPDDR5x, 273GB/s bandwidth (shared CPU/GPU) |
| Storage | 4TB self-encrypting NVMe PCIe Gen5 SSD |
| Networking | 2x NVIDIA ConnectX-7 (200Gbps each), Gigabit LAN, Wi-Fi 7 |
| Ports | 4x USB 3.2 Type-C, HDMI 2.1b |
| Operating System | NVIDIA DGX OS (Linux), pre-loaded with CUDA, PyTorch, NIM microservices, NeMo Framework |
| Security | Self-encrypting SSD, integrated Kensington lock slot, tamper-resistant sealed enclosure |
| Form Factor | Mini workstation, approximately 1.2kg |
| Clustering | Direct-connect two units for 405B-parameter class inference, or up to four units via a RoCE 200GbE switch |
Good to know
- Inference and fine-tuning run entirely on-device: nothing touches a third-party cloud, nothing is billed per token and nothing depends on an outside provider’s availability.
- Because CPU and GPU share the same 128GB pool, model size is not capped by discrete VRAM, so you can load large open-weight models whole and iterate on fine-tuning jobs.
Choose the GN100 when you want a GB10 system that scales from one unit to two directly connected units, or to a four-node cluster over a RoCE switch, rather than a single-box-only setup.