Spin up H100s 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.
$ brahmi launch --gpu h100 --count 8 --image pytorch-2.5Accelerators from
NVIDIA BlackwellNVIDIA HopperNVIDIA AdaAMD InstinctGPU lineup
From a single L4 for an embeddings endpoint to 8× B200 nodes for pre-training. Every instance ships with NVMe storage, high-core CPUs and CUDA or ROCm drivers preinstalled.
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
GPU infrastructure for builders, plus ready-to-use AI apps for teams.
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.
Reserved multi-node clusters of 16 to 4,096+ GPUs on non-blocking InfiniBand, with Slurm or Kubernetes managed for you.
Deploy open-weight or custom models behind an OpenAI-compatible API. Autoscale to zero and pay per request.
Dedicated, single-tenant servers with no hypervisor overhead for maximum performance and isolation.
Dedicated capacity in a region of your choice for data-residency, regulated and sovereign AI workloads.
AI-powered email automation. Draft, personalize and schedule emails and campaigns automatically.
sumscribe.brahmi.aiPricing
No egress fees, no hidden networking charges, no minimums on on-demand. Commit for a term and the rate drops.
| GPU | Memory | Bandwidth | FP8 TFLOPS | BF16 TFLOPS | Interconnect | On-demand | Reserved |
|---|
Why Brahmi
Instances boot in under a minute from warm pools. No quota tickets, no waiting lists for on-demand GPUs.
Rail-optimized InfiniBand fabric with GPUDirect RDMA, so multi-node training scales close to linearly.
Billing stops the moment your instance does. Zero data-transfer fees, ever.
Tuned images with CUDA, cuDNN, NCCL, PyTorch, vLLM and Triton, kept current so you skip the dependency fights.
Isolated VPCs, encrypted volumes, SSO and role-based access. Your data is never used to train anything.
Talk to infrastructure engineers, not ticket bots. Dedicated support on reserved and cluster plans.
Developers
Drive everything from the console, the CLI, a REST API or Terraform. Serverless endpoints speak the OpenAI API, so existing clients work unchanged.
# Launch an 8× H100 node with PyTorch preinstalled
brahmi launch \
--gpu h100 --count 8 \
--image pytorch-2.5 \
--volume datasets:/data
# Stream logs, then stop billing instantly
brahmi logs llm-train -f
brahmi stop llm-trainfrom brahmi import Brahmi
client = Brahmi()
node = client.instances.create(
gpu="h200",
count=8,
image="vllm-0.6",
region="in-west-1",
)
print(node.ssh_command)# OpenAI-compatible serverless inference
curl https://api.brahmi.ai/v1/chat/completions \
-H "Authorization: Bearer $BRAHMI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.3-70b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'Use cases
B200 and H200 clusters with InfiniBand for foundation-model runs.
B200 · H200 · MI300XLoRA to full fine-tunes on a single node, billed by the second.
H100 · A100High-throughput serving with vLLM, TensorRT-LLM and autoscaling.
H200 · L40S · L4Diffusion, video generation, rendering and VFX pipelines.
L40S · RTX 6000 AdaOn-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.
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.
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.
Every image includes up-to-date NVIDIA drivers, CUDA, cuDNN and NCCL (or ROCm on AMD). Choose from ready-made PyTorch, JAX, vLLM, TensorRT-LLM and Jupyter images, or bring your own container.
Brahmi capacity is deployed in Tier III+ facilities, with regions expanding through the year. Contact sales for current region availability and data-residency options.
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.
Get access to Brahmi today, or talk to us about reserved clusters for your team.