Best GPU for LLM Training in India
Find the best GPU for training large language models in India. VRAM requirements, GPU recommendations, and infrastructure considerations for LLM training at scale.
VRAM Requirements for Large Language Model (LLM) Training
Minimum VRAM
40 GB
Recommended VRAM
80 GB
Recommended GPUs
Key Considerations
- VRAM is the primary constraint. Training a 7B model requires at least 28 GB in FP16; a 70B model needs 280+ GB distributed across multiple GPUs. Budget for 80 GB per GPU minimum.
- NVLink and NVSwitch are essential for multi-GPU training. Tensor parallelism requires high-bandwidth GPU-to-GPU communication that PCIe cannot provide efficiently.
- For multi-node training, InfiniBand (HDR/NDR) networking is strongly recommended. Ethernet (RoCE) can work for smaller clusters but introduces latency overhead.
- Plan for sustained 24/7 power delivery. An 8x H100 SXM node draws approximately 10 kW. Indian data centres must provide adequate power density and cooling.
- Storage throughput matters. Use NVMe SSDs for training data to prevent GPU starvation. A parallel file system (Lustre, GPFS, or BeeGFS) is recommended for multi-node clusters.
What NOT to buy
Consumer GPUs like the RTX 4090 (24 GB VRAM is insufficient for meaningful LLM training, and NVIDIA's EULA prohibits data centre use). Also avoid GPUs without NVLink for multi-GPU training. PCIe bandwidth becomes a severe bottleneck during distributed training of large models.
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