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Παρακολουθούμενα είδη Σύνδεση
Micro tower server in dark finish with triple-fan graphics card visible through side panel

ABS

ABS AITPC265KF5070TI rack server Intel Core Ultra 7 265KF RTX 5070 Ti 32GB DDR5 2TB NVMe 320GB AI SSD

SKU CH-8WB8-ZG2H3 MPN AITPC265KF5070TI

Micro-tower server for AI training and fine-tuning with 20-core Intel Core Ultra 7, RTX 5070 Ti 16 GB GPU, 32 GB DDR5 6000 RAM, 2 TB NVMe SSD and 320 GB Phison aiDAPTIVCache AI SSD

  • WorkloadAI/ML/HPC Server
  • TypeMicro Tower
  • AI FeaturesAI Accelerated
  • CPU TypeIntel Core Ultra 7
  • CPU SpeedIntel Core Ultra 7 265KF
  • Number of CPU Cores20-Core (8P+12E) Processor
  • Trains and fine-tunes models on-premises with PHISON aiDAPTIV+ platform
  • 20-core Intel Core Ultra 7 265KF handles AI workloads
  • RTX 5070 Ti 16GB GPU accelerates training with DLSS 4
  • 32GB DDR5 6000MHz memory supports large model datasets
  • 2TB NVMe plus 320GB AI SSD provides fast storage

Purpose-built AI training server

The ABS AITPC265KF5070TI is a micro-tower server system configured for on-premises AI model training and fine-tuning. It combines an Intel Core Ultra 7 265KF 20-core processor with an MSI GeForce RTX 5070 Ti 16GB graphics card. The platform runs Ubuntu and includes the Phison aiDAPTIV+ software stack with a dedicated 320GB AI100E aiDAPTIVCache SSD.

Who this suits and who it does not

Researchers, developers and educational labs that need to train models such as Llama-2-13B or Stable Diffusion SDXL on private data without cloud costs will benefit from the dual inference and fine-tuning capability. Buyers who require multi-GPU scaling beyond a single card or enterprise-grade redundant power supplies should evaluate alternative rackmount clusters.

Installation notes

The unit arrives as a fully assembled micro-tower with the operating system and AI software stack pre-loaded on the 2TB M.2 NVMe primary drive. Memory expansion uses the two vacant DDR5 6000MHz slots up to a 256GB maximum. The 320GB aiDAPTIVCache occupies the second M.2 2280 slot, so additional high-speed storage requires a different interface.

Βασικά χαρακτηριστικά

  • Trains and fine-tunes models on-premises with PHISON aiDAPTIV+ platform
  • 20-core Intel Core Ultra 7 265KF handles AI workloads
  • RTX 5070 Ti 16GB GPU accelerates training with DLSS 4
  • 32GB DDR5 6000MHz memory supports large model datasets
  • 2TB NVMe plus 320GB AI SSD provides fast storage

Προδιαγραφές

BrandABS
SeriesAI TPC
ModelAITPC265KF5070TI
WorkloadAI/ML/HPC Server
TypeMicro Tower
AI FeaturesAI Accelerated
CPU TypeIntel Core Ultra 7
CPU SpeedIntel Core Ultra 7 265KF
Number of CPU Cores20-Core (8P+12E) Processor
Cache Memory30 MB Intel Smart Cache
# of Processors Installed1
MAX Processors1
Installed Memory Size32GB (2 x 16GB) DDR5 6000MHz
Memory TypeDDR5 6000MHz
Max Memory Capacity256GB
Number of Memory Slots4
SSD (Installed)SSD (Primary Storage): 2TB (M.2 Slot 1)
SSD (AI Acceleration): Phison aiDAPTIVCache AI100E M.2 2280 320GB (M.2 Slot 2)
Servers_Graphic InstalledMSI GeForce RTX 5070 TI 16G VENTUS 3X OC
GPU TypeNVIDIA GeForce RTX 5070 Ti
Video Memory16GB
LAN Ports1x 2.5Gbps
Operating SystemUbuntu
Βάρος αποστολής9.07 kg
Διαστάσεις συσκευασίας508 × 457 × 254 mm

Ερωτήσεις για αυτό το προϊόν

Do I need to supply my own DDR5 6000MHz modules to reach the 256GB maximum on the four DIMM slots?

The system ships with 32GB (2 x 16GB) DDR5 6000MHz installed; the four slots support up to 256GB total, so additional DDR5 6000MHz modules are required to reach the maximum.

Can the MSI GeForce RTX 5070 TI 16G VENTUS 3X OC run alongside an older GPU in the same chassis?

The micro-tower design and single installed GPU leave no dedicated second PCIe x16 slot for an additional graphics card.

How can I confirm the Phison AI100E 320GB aiDAPTIVCache is active after installing Ubuntu?

The PHISON aiDAPTIV+ software stack reports the AI100E cache status in its dashboard once the OS boots and the driver loads.

Επίσης σε αυτό το ράφι

Οι πελάτες σύγκριναν και αυτά

Ίδιο ράφι, ίδιο ταμείο — οκτώ νομίσματα και κλείδωμα ισοτιμίας 30 λεπτών.