To serve embedding models effectively, the hardware should balance computational power, memory, and scalability. Embedding models convert text, images, or other data into numerical vectors, which requires significant computation during inference. The ideal setup depends on the model size, expected traffic, and latency requirements. For most use cases, modern CPUs with multiple cores and GPUs for acceleration are recommended, paired with sufficient RAM and fast storage.
For CPU-based setups, multi-core processors like Intel Xeon or AMD EPYC are practical choices. These CPUs handle parallel inference tasks efficiently, especially for smaller models (e.g., sentence-transformers with up to 384 dimensions). If latency isn’t critical, CPUs can suffice and reduce costs. However, larger models (e.g., BERT-large or OpenAI embeddings) benefit from GPUs like NVIDIA A100, V100, or T4. GPUs accelerate matrix operations, which are central to transformer-based models. For example, an NVIDIA T4 (16GB VRAM) can handle batch inference for embeddings with 768 dimensions at low latency, making it suitable for real-time applications. Cloud providers like AWS (EC2 instances with T4 or A10G) or Google Cloud (A2 instances) offer preconfigured GPU options.
Memory and storage are also critical. Embedding models often require loading large pretrained weights (e.g., 500MB to 2GB per model), so sufficient RAM (32GB+) ensures smooth operation. Fast storage, such as NVMe SSDs, speeds up model loading and reduces cold-start delays. Network bandwidth matters for distributed setups: if deploying in a cluster (e.g., Kubernetes), ensure nodes have low-latency connections to avoid bottlenecks. For optimization, consider quantizing models (e.g., using 8-bit integers instead of 32-bit floats) to reduce memory usage and speed up inference without significant accuracy loss. Tools like ONNX Runtime or TensorFlow Lite can help with this. Finally, monitor resource usage and scale horizontally (adding more instances) or vertically (upgrading hardware) based on demand.