AI Training & Inference Server
Quad GPU On-Premises Large Language Model Training and Inference Server from Puget Systems Supporting Up to Four NVIDIA RTX 6000 Ada or A6000 GPUs
Enterprise AI Workloads. Workstation Hardware.
This rackmount workstation is built to host a web-based chat interface for large language models (LLMs) and supports multiple simultaneous users.
We have this exact system running at our office with a full set of four NVIDIA RTX 6000 Ada graphics cards. It is running Ubuntu 22.04 LTS with Meta’s Llama-2-70b-chat-hf, using HuggingFace Text-Generation-Inference (TGI) server and HuggingFace ChatUI for the web interface. This model uses approximately 130GB of video memory (VRAM), and the system should work with any other LLM that fits within available GPU memory (192GB with four cards installed).
This configuration was also tested with Falcon-40b, which needs less memory space and can run on just two RTX 6000 Ada GPUs. Besides running a chat interface, this hardware is also suitable for base model fine-tuning within the available GPU memory limits. Using a 4-bit quantized QLoRA should work well.
- Sample response times (single user):
- Validation Time = 0.59673 ms
- Queue Time = 0.17409 ms
- Time per Token = 54.558 ms
- Stress tested with multiple concurrent users
- Data below is from a session with 114 prompts over 5 minutes
- Average prompt response under multi-user load:
- Validation Time = 3.0312 ms
- Queue Time = 4687.9 ms
- Time per Token = 68.076 ms
Try it out for yourself at the Puget Systems SIGGRAPH 2023 booth (#630)!
System Specifications
| CPU | Intel Xeon W-3400 Processor |
| RAM | Up to 512GB DDR5 ECC |
| GPUs | Up to 4 NVIDIA RTX 6000 Ada or RTX A6000 |
| Storage | Up to 3 M.2 NVMe SSDs and 8 SATA drives |
| Chassis | SilverStone 5U Rackmount (convertible to tower) |
| Power Supply | 1300W + 750W Dual PSUs (requires two outlets on a 20A 120V circuit) |
| Operating System | Ubuntu 22.04 LTS Server |
Customize Your AI Server Today
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