Recently, I had the opportunity to test my PC’s capabilities by running ollama run deepseek-r1:32b, a large language model with 32 billion parameters. This presented a significant workload, and I was interested in observing how my system handled it.
Observations on System Performance
Running such a large model provides valuable insights into a system’s strengths. Here are some of my observations:
- Memory Management:
- Successfully running the model indicated that my PC has sufficient RAM (32 GB RAM) to handle substantial workloads. The system managed to load and process the model without encountering critical memory errors.
- Processing Capabilities:
- The Ryzen 9 5900X CPU, and the NIVDA Rog Strix 2080TI GPU, demonstrated the ability to handle the computational demands of the model. While inference speeds varied, the system maintained a reasonable level of responsiveness.
- Storage Performance:
- The speed of my storage drive contributed to efficient model loading times.
- System Stability:
- The system remained stable throughout the process, even under a heavy load.
What This Means
This experience highlights that my PC is capable of handling demanding tasks, including running large language models. It reinforces the importance of having a well-balanced system with adequate RAM, a capable processor, and fast storage.
Reflecting on System Capabilities
This exercise was a useful way to evaluate my PC’s performance and understand its strengths and limitations. It’s a reminder of the ongoing advancements in hardware and software, and the increasing accessibility of powerful computing capabilities.
My workstations specification:
- CPU: Ryzen 9 5900X processor
- GPU: NIVDA Rog Strix 2080TI – 11 GB
- RAM and SWAP: 32 GB RAM and 32 GB SWAP/paging
- DISK: 1 TB SD SATA, 1/2 TB SD SATA, 12 TB USB 3.1
- Clock: Standard clock speed