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 SXM reserved monthly pricing via ZenoCloud is ₹1,50,000/month ($1,800) for a single GPU, fully managed. This includes hardware, power, bandwidth, OS, CUDA stack, and 24/7 ops. On-demand H100 pricing is approximately ₹249/hour. Multi-node H100 cluster pricing is custom — contact us with your node count and training timeline for a scoping quote.

Yes — 5,000 INR in free GPU credits, no credit card required. Credits cover approximately 100 hours on an L4 GPU, 20 hours on an A100, or roughly 20 hours on an H100. We typically use trial credits to run a short validation training run (1–2 epochs on your dataset) to benchmark throughput and confirm the configuration before you commit to a reserved plan.