Best GPU for Recommendation Systems in India
Find the best GPU for training and serving recommendation models in India. Embedding table sizes, memory requirements, and GPU recommendations for e-commerce and content platforms.
VRAM Requirements for Recommendation Systems (DLRM, Two-Tower, Wide & Deep, Collaborative Filtering)
Minimum VRAM
24 GB
Recommended VRAM
80 GB
Recommended GPUs
Key Considerations
- Recommendation models like DLRM have massive embedding tables that can exceed 100 GB. Multi-GPU training with model parallelism (embedding sharding across GPUs) requires NVLink for efficient embedding lookup communication.
- For Indian e-commerce platforms (millions of SKUs, hundreds of millions of users), plan for A100 80 GB or H100 80 GB GPUs. The embedding tables alone for a large catalogue can consume 40-80 GB.
- Inference for recommendation systems is latency-critical. Users expect sub-100ms response times. Deploy inference on L40S or T4 GPUs with TensorRT optimisation. Keep hot embeddings in GPU memory and use CPU fallback for cold items.
- High-bandwidth CPU-GPU interconnect (PCIe Gen5) and fast NVMe storage matter for recommendation workloads. Embedding lookups for cold items may need to be fetched from SSD-backed storage.
- Consider NVIDIA Merlin framework for end-to-end recommendation pipelines. It handles data preprocessing, feature engineering, model training, and inference on GPU, dramatically reducing training time compared to CPU-only pipelines.
What NOT to buy
Avoid GPUs with less than 24 GB VRAM for recommendation model training. Embedding tables for large product catalogues (millions of items) consume enormous amounts of memory. Also avoid GPUs without efficient sparse operation support for embedding lookups.
Talk to us about your recommendation systems (dlrm, two-tower, wide & deep, collaborative filtering) setup
We'll recommend the right GPU and quote within 24 hours.