RTX AI Training Computer
AIT275060T
Produktbeschreibung
MODEL: EMB-AIT275060T
All-in-One. AI-First. Data-Sovereign. The EMB-AIT275060T is a compact 2.65-litre workstation engineered for small-scale full-lifecycle AI development, including LLM/VLM training, fine-tuning, and high-speed inference. Powered by the Intel Core Ultra 7 255HX processor and NVIDIA GeForce RTX 5060 Ti 16GB GPU with Phison aiDAPTIVCache AI100 SSD technology, it delivers data-center-class AI capabilities in a desktop form factor — with zero cloud dependency.
Run Massive Models on Consumer Hardware: The aiDAPTIVCache AI100 pSLC SSD (rated 100 DWPD) acts as an AI cache extension for GPU memory, enabling models from 24GB to 120GB+ to run on a single 16GB VRAM GPU. Accelerates long-context inference by approximately 10x compared to standard RAM offloading.
FEATURE HIGHLIGHTS
● Intel Core Ultra 7 Processor 255HX, 20-core, up to 5.2 GHz, 30MB cache
● NVIDIA GeForce RTX 5060 Ti 16GB GDDR7 PCIe graphics card
● 32GB DDR5-6400 SO-DIMM system memory for high-bandwidth AI workloads
● 2TB M.2 2280 NVMe SSD for OS and application storage
● 320GB Phison aiDAPTIVCache AI100 pSLC SSD, rated for 100 DWPD, serves as AI cache / swap space for GPU memory
● Smart SSD offloading: transparently offloads gradients, MoE (Mixture of Expert), and KV-cache to AI SSD with minimal performance impact
● Run and fine-tune AI models typically requiring 24GB to 48GB+ VRAM on a single consumer GPU
● Quad independent display outputs: 1 x HDMI 2.1 + 3 x DP 1.4
● High-speed I/O: 5 x USB 3.2 (10 Gbps) + 2 x Thunderbolt 4
● Dual 2.5 Gbps Ethernet ports for high-speed networking
● Intel Wi-Fi 7 (802.11be) + Bluetooth 5.4 wireless connectivity
● Compact chassis: 210 x 203 x 62.2 mm (2.65 Litres)
● 330W AC-DC power adapter (20V)
● Supports Microsoft Windows 11 and Ubuntu 24.04.3 or newer
● aiDAPTIVLink middleware integrates with PyTorch for training and inference
● Data sovereignty: eliminates cloud dependency with secure on-premises AI processing
Produktspezifikationen
APPLICATIONS
Knowledge RAG (Retrieval-Augmented Generation) Inference
Serve AI assistants tuned to your business or curriculum using local data, without exposing sensitive information to third-party clouds. Ideal for organizations requiring strict data privacy.
Domain-Specific Copilots & Chatbots
Run retrieval-augmented generation pipelines on-premises to answer questions from internal documents, manuals, research papers, or records while keeping all content private and secure.
Coding Assistants & Developer Tools
Host local code copilots that understand your repositories, build systems, and internal libraries. Full codebase awareness without sending proprietary source code to external AI services.
Agentic & Long-Context Workflows
Support multi-step AI agents, longer session histories, and richer tool use by giving models more working memory through aiDAPTIVCache SSD offloading without sacrificing latency.
Learning & Experimentation
Give teams and students a hands-on environment to explore LLM behavior, safety, fine-tuning, and evaluation using real workloads on local hardware. Perfect for AI research labs and educational institutions.
LLM Fine-Tuning & Training
Full-lifecycle AI development from model fine-tuning to deployment on a single workstation. Leverages gradient offloading and KV-cache management to train larger models than VRAM alone would allow.
| Processor | Intel Core Ultra 7 Processor 255HX 20 cores, up to 5.2 GHz, 30MB cache |
| Graphics | NVIDIA GeForce RTX 5060 Ti 16GB GDDR7, PCIe interface |
| System Memory | 32GB DDR5-6400 SO-DIMM |
| OS Storage | 2TB M.2 2280 NVMe SSD |
| AI Cache | 320GB Phison aiDAPTIVCache AI100 pSLC SSD Rated for 100 DWPD (Drive Writes Per Day) Acts as AI cache / swap space for GPU memory |
| Video Output | 1 x HDMI 2.1 3 x DP 1.4 Supports up to 4 independent displays |
| Video Output | 2 x HDMI 2.1 (up to 8K 60Hz) 1 x USB-C DP (DisplayPort Alt Mode) |
| USB Ports | 5 x USB 3.2 (10 Gbps) 2 x Thunderbolt 4 |
| LAN | 2 x 2.5 Gbps Ethernet ports |
| Wireless | Intel Wi-Fi 7 IEEE 802.11be Bluetooth 5.4 |
| Dimensions | 210 (L) x 203 (W) x 62.2 (H) mm 2.65 Litres |
| Operating Temperature | 0°C to +38°C |
| Power Input | 20V / 330W AC-DC power adapter |
| Supported OS | Microsoft Windows 11 Ubuntu 24.04.3 or newer |
| Software Stack | aiDAPTIVLink middleware (integrates with PyTorch) NVIDIA CUDA Toolkit NVIDIA cuDNN |
| AI Offloading Capabilities | Gradient offloading MoE (Mixture of Expert) offloading KV-cache offloading |
| Supported AI Models | Llama-2-13b-hf, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Mistral-7B-Instruct-v0.1, gemma-2-9b-it, gemma-3-1b-it, Qwen2.5-14B/7B/3B/1.5B-Instruct, Qwen3-14B/8B, Phi-4, Phi-4-mini-instruct, gpt-oss-20b, gpt-oss-120b And other models compatible with aiDAPTIVCache technology |
AI PERFORMANCE COMPARISION
| Feature | Standard VRAM-Only Inference | aiDAPTIVCache AI Cache Drive |
|---|---|---|
|
Model Size Limit |
Hard-capped by physical VRAM (e.g., 16 GB for RTX 5060 Ti) | Virtually extended; can run 120B models on a single 16 GB VRAM GPU with 32 GB system memory |
| Performance | Highest throughput and lowest latency; no swapping overhead | Up to 10x faster than standard system-RAM-only offloading |
| Cost | High; requires multiple data-center GPUs for large models | Cost-effective; uses affordable SSDs to mimic high-end GPU memory pools |
| Data Handling | Model must fit entirely in VRAM for full speed | Model is sliced and swapped between SSD cache and GPU VRAM as needed |
Note: KV (Key-Value) cache is a crucial optimization technique in transformer-based LLMs used to accelerate the inference (generation) phase. DWPD = Drive Write Per Day is an SSD endurance measurement defined with 5-year warranty.
ORDERING PART NUMBER (SKU)
| SKU | BOM | Description | Power Adapter |
|---|---|---|---|
| EMB-AIT275060T-A0 | 250-FB603-000EB |
AI Training PC, Intel Core Ultra 7 255HX,RTX 5060 Ti 16GB, 32GB DDR5,2TB NVMe, 320GB aiDAPTIVCache, Wi-Fi 7, 330W |
US / EU / UK Plug |
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