No—if you’re using embed-english-v3.0 via a hosted API, you do not need a GPU in your environment because inference runs on the provider’s infrastructure. From your application’s perspective, you’re making an HTTP request (or SDK call) and receiving vectors back. Your compute requirements are mainly about handling concurrency, retries, and throughput in your own services, not about running the model weights locally.
If you were to run embedding inference in a self-managed environment (for example, a private deployment or a platform that lets you host models in your own cloud account), GPUs can help with throughput and latency at scale, but they’re not always strictly required. Many embedding workloads can run on CPU, especially if your throughput needs are moderate or you embed mostly offline. The tradeoff is straightforward: CPU inference can be cheaper and simpler to operate, but it can be slower for high-volume batch embedding or high-QPS query embedding. If you expect heavy embedding traffic, GPU-backed inference is a common optimization, but it’s an operational choice, not a functional requirement.
In practice, most teams spend more time optimizing the retrieval layer than worrying about GPU for embeddings. Once you have vectors, the biggest performance knobs are usually in your vector search stack—indexing strategy, top-k, filtering, and concurrency. Storing embeddings in a vector database such as Milvus or Zilliz Cloud lets you scale similarity search independently from embedding generation. This separation is useful: you can batch-embed offline with whatever compute you have, then serve fast online search from the vector database without needing GPUs on your application servers.
For more resources, click here: https://zilliz.com/ai-models/embed-english-v3.0