GPU Catalog
| Component ID | Manufacturer | Model | Quantity | VRAM | Interface | Compute Cap | Notes |
|---|---|---|---|---|---|---|---|
| GPU-001 | NVIDIA/Dell | Quadro 600 | 3 | 1 GB | PCIe | sm_21 | |
| GPU-002 | EVGA | GTX 770 | 1 | 2 GB | PCIe | sm_30 | Requires 6+8 pin power |
GPU Placement
| Asset ID | Hostname | Component ID | Slot / Location | Role | Notes |
|---|
GPU Overviews
Here are some brief overviews of the GPUs in the inventory, highlighting their typical uses and characteristics.
NVIDIA Quadro 600 (e.g., GPU-001)
96 CUDA cores · 1GB GDDR3 · low-profile · 40W TDP
The NVIDIA Quadro 600 is an entry-level professional graphics card from the Fermi generation (circa 2010-2011). Designed primarily for CAD, DCC (Digital Content Creation), and basic scientific visualization, it is not optimized for gaming workloads. Equipped with 1GB of VRAM and typically presented in a low-profile form factor, these cards are well-suited for providing display output in servers that lack integrated graphics, or for light compute tasks that can utilize NVIDIA’s CUDA architecture, though performance will be limited by their vintage.
EVGA GTX 770 (e.g., GPU-002) — NVIDIA specs
1536 CUDA cores · 2GB GDDR5 256-bit · 3.2 TFLOPS · 230W TDP · Compute Capability sm_30
The NVIDIA GeForce GTX 770, frequently available in variants such as the EVGA GTX 770, was a high-end gaming graphics card released in 2013, based on the Kepler architecture. Featuring 2GB (or 4GB) of GDDR5 VRAM, it delivered strong performance for its release era. In a homelab setting, a GTX 770 can be repurposed for tasks like video transcoding, entry-level machine learning experiments, or providing robust graphical output for a dedicated workstation attached to a server. Its requirement for external power connectors (typically 6+8 pin) signifies its higher power consumption profile.
CUDA compatibility: sm_30 (Kepler) is below the minimum for most current ML tooling — PyTorch 2.x requires sm_37, vLLM requires sm_70, and pre-built Ollama packages target sm_50+. GPU-accelerated inference with off-the-shelf tools is unlikely without custom builds. CPU fallback is the practical path.