Hardware recommendations for a data‑science workstation
| Component | Typical recommendation | Why it matters |
|---|---|---|
| CPU | 32‑core Intel Xeon W or AMD Threadripper PRO (or higher if you have very data‑parallel workloads) | High core count + fast memory access → better for ETL, cleaning, EDA. 16 cores is the minimal practical baseline. |
| GPU | NVIDIA GPU (e.g., RTX Pro 6000 Blackwell with 96 GB VRAM or H200 for NVLink) | Most data‑science libraries (RAPIDS, cuDF, Modin on Intel oneAPI) are NVIDIA‑centric. 96 GB gives headroom for large feature spaces; multiple GPUs help if you need more memory or multi‑GPU ML training. |
| RAM | 1–2 TB of system memory (or at least enough to hold your largest in‑core dataset) | Many data‑science tasks benefit from loading the entire dataset into RAM; out‑of‑core tools exist but are slower. |
| Storage | Fast NVMe SSDs for active working space (≥ 1–2 TB each, 8 TB max per drive); SATA SSDs or large platter drives for archival data | Data streaming can be a bottleneck; NVMe keeps I/O from throttling CPU/GPU work. RAID arrays can expand capacity but use up slots that might otherwise host GPUs. |
| Networking | 10 GbE NIC (or higher on rack‑mount servers) if you’ll pull data from network storage | Enables efficient access to shared datasets without bottlenecking the local I/O subsystem. |
Bottom line: For most modern data‑science workloads, aim for a workstation with a high‑core Intel or AMD CPU, an NVIDIA GPU with ≥ 24 GB (ideally 96 GB) VRAM, 1–2 TB of RAM, fast NVMe storage, and good network connectivity. Adjust upwards if your datasets routinely exceed these limits.
