Train and Fine-Tune AI Models on H100 GPUs in India

Pre-training from scratch means training a model on a large corpus from random weights — extremely GPU-intensive (tens of thousands of H100 hours for a 70B model). Fine-tuning starts from an existing checkpoint and trains on a smaller, task-specific dataset. LoRA and QLoRA are parameter-efficient fine-tuning techniques that reduce VRAM requirements significantly. Most enterprise use cases are fine-tuning, not pre-training.

LoRA fine-tuning of Llama 3.1 70B (QLoRA 4-bit): single A100 80GB. Full fine-tuning of Llama 3.1 70B (FP16 with FSDP): 4x A100 80GB or 2x H100 SXM. Full fine-tuning with DeepSpeed ZeRO-3: possible on 2x A100 80GB with gradient checkpointing. We recommend the right configuration after a 15-minute scoping call based on your dataset size and training timeline.

NVLink is NVIDIA's high-speed intra-node GPU interconnect with 900 GB/s bandwidth on H100 SXM (versus ~64 GB/s for PCIe). For multi-GPU training with all-reduce operations (DDP, FSDP), NVLink dramatically reduces communication bottleneck. On a 4x H100 NVLink node, all-reduce latency is roughly 5x lower than PCIe — which translates directly to faster training iteration time on large batch sizes.

We pre-configure checkpoint callbacks for HuggingFace Trainer and PyTorch Lightning. Checkpoints are written to local NVMe at configurable step intervals. We allocate sufficient storage for 3–5 checkpoint slots and alert if disk pressure threatens checkpoint writes. For long multi-day runs, we set up automatic checkpoint archival to object storage so you retain full training history.

Yes. NCCL is pre-configured for NVLink bandwidth on multi-GPU nodes. DeepSpeed ZeRO stages 1, 2, and 3 all work. PyTorch FSDP with full sharding and gradient checkpointing works. We validate your distributed training config during onboarding and benchmark a short training run before handing over the full job. Common issues (NCCL timeout config, NUMA binding, OOM with activation recomputation) are handled by our team.

H100 80GB monthly pricing via ZenoCloud is ₹1,80,000/month ($2,099) per node — a 1-month minimum commitment covering hardware, power, bandwidth, OS, and CUDA stack. That works out to roughly ₹247/hour effective (monthly ÷ 730), shown for comparison only; there is no hourly product. The managed ops add-on is ₹15,000 ($179) per node/month. Multi-node H100 cluster pricing is custom — contact us with your node count and training timeline.

Yes. We provision a trial node so you can test on the actual hardware before committing to a month. We typically use the trial to run a short validation training run on your dataset to benchmark throughput and confirm the configuration before you commit to a monthly node.