Best Practice – GPU Infrastructure for LLM Training in India

Large Language Model – LLM training is no longer reserved for a handful of tech giants. Across India, startups, research labs, and enterprises are now building and fine‑tuning their own models for search, customer support, analytics, and domain‑specific copilots. Yet the core bottleneck remains the same: access to the right GPUs and a robust training infrastructure.

This article explains what kind of GPUs are actually needed for LLM training (not just inference), how to think about capacity planning from 7B to 70B+ models, and how HostGenX can power that journey with GPU‑ready data centers and sovereign cloud infrastructure in India.

Why GPUs matter for LLM training

Training an LLM is a massively parallel numeric computation problem. GPUs excel at this because they pack thousands of cores optimized for matrix multiplications, exactly what transformer models repeatedly perform in attention and feed‑forward blocks. CPUs, by contrast, are designed for general‑purpose logic and quickly become a bottleneck when scaling to billions of parameters and trillions of tokens.

Modern LLM training stacks (PyTorch, JAX, DeepSpeed, Megatron‑LM, etc.) are built to exploit GPU features like tensor cores, mixed‑precision (FP16/BF16), and high‑bandwidth memory (HBM). Without GPUs that support these capabilities efficiently, training times explode, costs skyrocket, and experimentation speed drops so low that many projects never reach production.

Key GPU specs that actually matter

Not every “AI GPU” is equal when the goal is LLM training rather than small‑scale inference. The most important GPU characteristics to evaluate are:

• VRAM capacity:
  LLMs keep model weights, activations, and optimizer states in GPU memory during training. For serious work, 24 GB is a bare minimum; 40–80 GB per GPU is the current sweet spot for 7B–70B models, often spread across multiple GPUs.
• Memory bandwidth:
  Large transformer layers are memory‑bound. High‑bandwidth memory (HBM) on data center GPUs such as NVIDIA A100/H100 or AMD Instinct MI300 lets you feed the compute units fast enough to keep utilization high.
• Tensor performance (FLOPS):
  LLM training relies on dense linear algebra. GPUs with strong FP16/BF16 and tensor core performance dramatically reduce training time per step, which compounds over billions of tokens.
• High‑speed interconnects:
  When you train on multiple GPUs, interconnects like NVLink, NVSwitch, and high‑speed InfiniBand or RoCE become critical to synchronize gradients and shard model states efficiently. Weak networking turns your “cluster” into an idle parking lot of GPUs.
• Ecosystem and software support:
  Support for CUDA, ROCm, NCCL, container runtimes, and orchestration (Kubernetes, Slurm, etc.) determines how easily you can scale from a prototype to a multi‑node training job.

GPU classes for different LLM workloads

Here’s a practical way to think about which GPU class fits which LLM use case.

1. Frontier‑scale and foundation models

If you are training large, general‑purpose LLMs (tens of billions of parameters and beyond) or multilingual foundation models, you need top‑tier data center GPUs with high VRAM, HBM, and strong interconnects:

• NVIDIA H100 / H200 / B200
  These are currently the most popular GPUs for state‑of‑the‑art LLM training, offering high BF16 throughput and 80 GB+ of HBM per GPU.
• NVIDIA A100 40/80 GB
  Still widely used and highly capable, especially in clusters of 8–16 GPUs per node with NVLink and fast storage.
• AMD Instinct MI300‑class
  Growing in adoption, especially in HPC and cost‑sensitive deployments, with competitive HBM capacity and strong transformer performance.

Global‑scale LLMs like GPT‑4 and Llama 3 were reportedly trained on tens of thousands of A100/H100 GPUs, showing the level of compute needed at the frontier.​

2. Domain‑specific and mid‑scale models

For many Indian enterprises, the goal is not a general‑purpose trillion‑parameter model, but a strong domain‑specialized model (for BFSI, healthcare, logistics, legal, etc.) in the 7B–30B range:

• Clusters of A100/H100 or L40S‑class GPUs can comfortably train or fully fine‑tune such models using a mix of tensor/model/data parallelism.
• You can also experiment with parameter‑efficient finetuning (PEFT), LoRA, and QLoRA to reduce GPU memory needs, but data center GPUs still provide far better throughput and stability.

3. Prototyping, R&D, and local fine‑tuning

Smaller teams or early‑stage experiments can start with high‑end workstation or consumer GPUs:

• RTX 4090, RTX 4080, RTX 6000 Ada with 16–24 GB VRAM work well for fine‑tuning 7B‑class models, especially with LoRA/QLoRA and 4‑bit quantization.
• Budget options like RTX 3060/3070 can still be used for smaller models or educational workloads in India, as demonstrated in recent research on local LLM deployment.

However, these GPUs hit limits quickly when you move from experiments to production‑grade training on larger datasets and model sizes.

Model size vs GPU need: practical mapping

The exact number of GPUs and VRAM you need depends on sequence length, optimizer, batch size, and parallelism strategy. Still, some widely cited configurations give a realistic picture:

• A 7B parameter model can be fine‑tuned using 1–2 GPUs with 24–40 GB VRAM (e.g., 2× A5000/4090 or 1× A100 40 GB), especially with PEFT.
• A 13B–34B model typically benefits from 4–8 GPUs with 40–80 GB each, especially for full‑parameter training or long context lengths.​
• A 70B‑class model often requires 8–16 A100/H100‑class GPUs with 80 GB HBM each for practical training times and reasonable batch sizes.​

A recent hardware guide shows that training 1 trillion tokens on a mid‑size LLM can take around a month even on 8× A100 40 GB, underlining why GPU choice and cluster sizing matter.

Beyond GPUs: the rest of the LLM training stack

GPUs are only one part of the equation. High‑quality LLM training infrastructure must also consider:

• CPU and RAM: High‑core count CPUs (e.g., EPYC/Xeon) and 128–512 GB+ RAM per node for data preprocessing, data loaders, and distributed training coordination.
• Storage: NVMe SSDs with several TBs capacity for datasets, checkpoints, and logs; plus backup and archival tiers.
• Networking: 10–100 Gbps+ Ethernet or InfiniBand for scaling across nodes without starving GPUs while syncing gradients.
• Orchestration: Containerized environments (Docker, Kubernetes, Slurm) with GPU passthrough make it easier to schedule multi‑tenant training workloads.

Misconfiguring any of these layers can reduce effective GPU utilization dramatically, wasting expensive hardware.

The India context: LLM compute onshore

India is investing heavily in domestic AI compute capacity through initiatives like the IndiaAI Mission and national GPU clusters. The country has already crossed 34,000 GPUs in common compute capacity, with more on the way, reflecting strong demand for on‑shore, compliant, and low‑latency training infrastructure.

At the same time, many Indian teams struggle to get sustained access to hundreds or thousands of high‑end GPUs with predictable performance. Reports highlight that some “LLM‑ready” offerings mix in lower‑end GPUs (like L4/L40 without strong interconnects) that are better suited to inference than to large‑scale LLM training. This makes provider choice critical.

This is where specialized GPU‑ready data centers like HostGenX become important: they can align hardware architecture, networking, and compliance requirements specifically for GenAI workloads in India.

How HostGenX is built for LLM training in India

HostGenX operates GPU‑powered, enterprise‑grade data centers in India designed for AI, ML, and high‑performance workloads. The platform offers future‑ready GPU and bare‑metal servers with low‑latency connectivity and strict compliance, making it a strong foundation for LLM training projects.

Key capabilities relevant to your LLM roadmap include:

• Access to modern NVIDIA GPUs:
  HostGenX provides NVIDIA A100, H100, and RTX 4090‑class GPUs, giving teams options from R&D and fine‑tuning to multi‑node training jobs.
• GPU‑ready Tier III/IV infrastructure:
  Data centers are engineered with redundant power, advanced cooling, and carrier‑neutral connectivity, enabling stable, long‑running training jobs with 99.99% uptime SLAs.

Because the infrastructure is located within India, HostGenX helps organizations meet data residency, sovereignty, and sector‑specific compliance needs while still tapping into cutting‑edge GPU resources.

How HostGenX accelerates your LLM training lifecycle

From a practical engineering standpoint, HostGenX can help at multiple stages of your LLM lifecycle.

1. Prototyping and experimentation

For early experiments with 7B‑class models, instruction‑tuning, or RLHF, you can:

• Spin up single or small clusters of A100/4090 GPUs to evaluate datasets, architectures, and training recipes.
• Use containerized environments with GPU passthrough to quickly iterate on PyTorch or JAX code, without wrestling with drivers and CUDA versions.

This keeps your experimentation loop fast while spending only for the capacity you actually use thanks to transparent pay‑as‑you‑go pricing.

2. Scaling to production‑grade training

When you are ready to push to larger models, longer context, or higher data volumes:

• Deploy multi‑GPU, multi‑node clusters with A100/H100 GPUs connected via high‑speed networking suitable for data/model/tensor parallelism.
• Integrate checkpointing, distributed training frameworks, and monitoring into HostGenX bare‑metal or cloud environments so you can run weeks‑long training jobs reliably.

Because HostGenX infrastructure is tuned for AI workloads (compute, network, storage), you can aim for high GPU utilization and predictable training timelines instead of fighting noisy neighbors or underpowered links.

3. Fine‑tuning and inference on the same platform

Many teams want to keep training and inference within the same environment for latency, security, and cost reasons:

• Train or fine‑tune your LLM on HostGenX GPU servers, then deploy optimized inference endpoints in the same data center for low‑latency serving to Indian users.
• Use more cost‑efficient GPU tiers (e.g., 4090 or L40S‑class where appropriate) for inference while reserving A100/H100 clusters for intensive training runs.

This reduces data transfer, simplifies compliance, and lets you reuse monitoring and observability tooling across both training and production.

Why choose HostGenX over generic cloud for LLMs?

Several factors make HostGenX a strong fit for LLM training and fine‑tuning in India:

• Sovereign and compliant by design:
  HostGenX markets itself as a sovereign, compliance‑ready cloud and colocation provider, helping regulated sectors like BFSI and healthcare keep data within India.
• GPU‑first architecture:
  Future‑ready GPU and bare‑metal servers are a core offering, not an afterthought, which is crucial for predictable LLM training performance.
• Cost efficiency and predictable TCO:
  HostGenX highlights transparent pay‑as‑you‑go pricing and claims up to 50% lower total cost of ownership compared to on‑prem alternatives, which is critical when booking large GPU clusters over months.

For Indian startups and enterprises building GenAI products, this combination—high‑end GPUs, domestic data centers, and AI‑oriented design—makes HostGenX a compelling infrastructure partner.

Getting started: mapping your LLM needs to HostGenX

To translate the theory into an actual deployment, you can think in terms of three steps:

1. Define your model and training goal:
   • Are you fine‑tuning a 7B‑class model for customer support in one language, or training a 30B multilingual foundation model?
   • Do you need full‑parameter training or will LoRA/QLoRA suffice?
2. Estimate GPU and infra requirements:
   • Use published guides and simple sizing rules (e.g., 2–4 A100s for small models, 8–16 for 30B–70B) as a baseline, then factor in dataset size and sequence length.
   • Consider storage (1–5 TB+), RAM (128 GB+), and network (10–100 Gbps) to keep GPUs fully utilized.
3. Engage HostGenX for the right cluster shape:
   • Work with HostGenX to provision the right mix of GPUs (A100/H100/4090), bare‑metal nodes, and networking based on your training plan.
   • Leverage their colocation and managed hosting options if you already own part of the hardware stack but need secure, reliable rack space in India.

With the right mapping between model ambition and infrastructure, LLM training becomes a manageable engineering project rather than an open‑ended cost sink.

In summary, effective LLM training demands more than just “some GPUs.” It requires high‑VRAM, high‑bandwidth accelerators like NVIDIA A100/H100 or AMD MI300, backed by strong networking, storage, and orchestration. For teams in India, HostGenX provides exactly this blend: GPU‑powered, sovereign data centers and cloud infrastructure purpose‑built for AI, enabling you to prototype, scale, and serve your LLMs without leaving the country’s borders.