Spin up Blackwell GPUs in seconds, scale to thousands of GPUs on InfiniBand, and pay only for the seconds you use. Brahmi gives AI teams frontier compute without the hyperscaler overhead.
Explore our sovereign, high-performance AI solutions
Six ways to run AI on Brahmi, from shared classroom GPUs to dedicated training clusters, all hosted in India.
Education & research
Brahmi AI Lab
Managed GPU environments for education and research, with the tools and monitoring already set up.
Virtual workstations run on sliced NVIDIA GPUs, so many learners can share one high-end card. Each workspace comes ready to use and has storage attached. It suits university AI centres of excellence, training programmes and R&D teams fine-tuning open models.
RTX PRO 6000 Blackwell MIG slices (24 or 48 GB) or full 96 GB GPUs
Jupyter, VS Code, CUDA, PyTorch and TensorFlow preinstalled, plus curated datasets
Unlimited users, concurrency caps and calendar-based scheduling
Role-based access, with live tracking of GPU usage and learner activity
Containerised workstations start in seconds and release resources when idle
Shared and per-user storage, with external storage linking
Training · Fine-tuning · Inference
Brahmi AI Workspace VM
High-performance GPU virtual machines that you can launch on demand in minutes.
These GPU-backed VMs handle training, fine-tuning, inference and HPC. Start with one GPU and scale up to multi-GPU machines. Workspaces are built to be shared across a team, and you can bring your own environment.
NVIDIA B200 and RTX PRO 6000 Blackwell, from a single MIG slice to 8-GPU VMs
One-click instances, managed through a self-service console
RBAC, multi-tenancy, project-level segregation and audit logs
Local, object and network-attached storage
Hourly on-demand billing or fixed-term subscriptions
Free data ingress and egress
Foundation models · HPC
Brahmi Bare Metal
Dedicated, tuned GPU servers with no virtualisation overhead.
You get single-tenant servers with direct access to the hardware and no oversubscription. HGX nodes handle large-scale training and RTX PRO 6000 nodes handle inference. Start with one node and grow to a full cluster.
8× NVIDIA HGX B200 with NVLink 5 and NVSwitch, up to 1.8 TB/s GPU-to-GPU
HGX B300 and RTX PRO 6000 Blackwell configurations
InfiniBand NDR 400G and 400GbE networking for multi-node scale-out
NVIDIA BlueField-3 DPUs included
NVMe local storage, plus parallel file system and object storage options
NVIDIA AI Enterprise stack: CUDA, TensorRT, RAPIDS, PyTorch and TensorFlow
Large-scale training
Brahmi Cluster
Kubernetes and Slurm clusters built for large-scale AI training and inference.
These are bare-metal GPU clusters, with orchestration managed for you. Slurm handles HPC-grade scheduling for long training runs, batch jobs and simulations. Kubernetes runs inference, APIs and containerised pipelines, with elastic scaling.
HGX B200 / B300 nodes and GB200 / GB300 NVL72 racks on a non-blocking InfiniBand fabric
Scales from 2 nodes to hundreds without disrupting running work
Fair-share scheduling, namespaces and network segmentation keep tenants isolated
Standard or high-availability Kubernetes control planes
Grafana dashboards for GPU, CPU, power, network and storage metrics
High-speed parallel file system and S3-compatible object storage
LLM · Vision · Speech
Brahmi AI Endpoint
Deploy LLM, vision and speech models right away on autoscaling serverless inference with an OpenAI-compatible API.
Endpoints run on low-latency GPUs and are built on NVIDIA NIM. One place covers deploying, fine-tuning, running inference and monitoring. You pay per token and can switch from existing OpenAI-style code with a few lines.
Curated marketplace of open models: Llama, Qwen, DeepSeek, Gemma, Mistral and more
One inference layer for text, vision and speech (ASR / TTS)
TensorRT and quantisation for higher throughput and concurrency
Autoscaling on request volume, with rate limits for fair use
API-key authentication, encryption in transit and logical isolation
Usage, latency and SLA monitoring, plus hybrid on-prem and private-cloud inference
Bursty & batch workloads
Brahmi Serverless GPUs
On-demand GPU power with nothing to provision. You pay only for execution time and can scale right away.
Run your own containers for training, fine-tuning and deployment without managing any hardware. Cold starts are low, and you control latency, time-to-first-token and concurrency at the application level.
Custom containers, with Docker and Kubernetes-native deployment
Fractional GPUs for fine-tuning and inference
Scales to zero when idle and up to multiple GPUs (B200 / RTX PRO 6000) under load
Min / max pod limits with combined triggers such as latency and memory
Modular pipelines: LoRA adapters, message queues and model ensembles
Works with PyTorch, TensorFlow, ONNX Runtime, Hugging Face and NGC containers
GPU lineup
Pick the right silicon for every workload
From a 24 GB MIG slice for an embeddings endpoint to GB300 NVL72 racks for pre-training. Every instance ships with NVMe storage, high-core CPUs and CUDA drivers preinstalled.
On-demandReserved save up to 30%
Show all prices
Prices are per GPU-hour in Indian Rupees (INR), exclusive of GST. Reserved rates reflect a 12-month commitment; longer terms and volume pricing available from sales.
Products
One platform, from prototype to production
GPU infrastructure for builders, plus ready-to-use AI apps for teams.
On-demand GPU instances
Single-GPU to 8-GPU virtual machines with root access, persistent volumes and SSH or Jupyter out of the box. Start, stop and resize whenever you like with per-second billing.
1×, 2×, 4× and 8× configurations
Prebuilt PyTorch, JAX, vLLM and TensorRT-LLM images
Snapshots & persistent NVMe volumes
GPU clusters
Reserved multi-node clusters of 16 to 4,096+ GPUs on non-blocking InfiniBand, with Slurm or Kubernetes managed for you.
Serverless inference
Deploy open-weight or custom models behind an OpenAI-compatible API. Autoscale to zero and pay per request.
Bare metal
Dedicated, single-tenant servers with no hypervisor overhead for maximum performance and isolation.
Private cloud
Dedicated capacity in a region of your choice for data-residency, regulated and sovereign AI workloads.
On-demand instances are billed per second from the moment they start until they stop, with no minimum. Storage is billed per GB-month. There are no egress or ingress charges.
What's the difference between on-demand and reserved?
On-demand gives you full flexibility with no commitment. Reserved capacity locks in dedicated GPUs for a term (typically 1 to 36 months) at a significantly lower hourly rate, and guarantees availability.
Can I run multi-node distributed training?
Yes. Our cluster product connects 8-GPU nodes over a non-blocking InfiniBand fabric with GPUDirect RDMA. We can provide Slurm or managed Kubernetes on top, or hand you bare metal.
Which software comes preinstalled?
Every image includes up-to-date NVIDIA drivers, CUDA, cuDNN and NCCL. Choose from ready-made PyTorch, JAX, vLLM, TensorRT-LLM and Jupyter images, or bring your own container.
Where are your data centers?
Brahmi capacity is deployed in Tier III+ facilities, with regions expanding through the year. Contact sales for current region availability and data-residency options.
Is my data secure?
Workloads run in isolated networks with encrypted storage. We never access or train on customer data. Dedicated single-tenant bare metal and private cloud options are available for stricter requirements.
Your next model deserves better compute.
Get access to Brahmi today, or talk to us about reserved clusters for your team.
