Hardware
Decoupling embedding from ingestion means whatever hardware is on hand can do the work. One script, runtime device check: MPS on Apple Silic…
Decoupling embedding from ingestion means whatever hardware is on hand can do the work. One script, runtime device check: MPS on Apple Silicon, CUDA on NVIDIA, CPU fallback otherwise. 10K-20K records
Decoupling embedding from ingestion means whatever hardware is on hand can do the work. One script, runtime device check: MPS on Apple Silicon, CUDA on NVIDIA, CPU fallback otherwise. 10K-20K records per Parquet partition, split across machines, merged, bulk imported. 🔗 https://www.pinecone.io/blog/generating-test-data-for-pinecone/
Related
- Systematic Optimization of Real-Time Diffusion Model Inference on Apple M3 Ultra
- llamacpp on Apple Silicon, once configured correctly is really rock solid! You can throw anything at it and it will answer. Impressive!
- Great collab with @SakanaAILabs on an #ICML26 paper about sparse transformer kernels + formats optimized for modern NVIDIA GPU execution. • …
Source: Pinecone (X) | 2026-07-22