What We Offer
LLM Deployment
Run models on your own infrastructure
- Deploy open-source LLMs (LLaMA, Mistral, Falcon, etc.)
- On-prem or private cloud deployment
- Model versioning and rollback support
- API gateway for model serving
GPU Workload Management
Maximize GPU utilization and throughput
- Kubernetes GPU scheduling and resource allocation
- Multi-GPU and multi-node training setups
- Dynamic scaling based on inference load
- GPU monitoring and cost optimization
MLOps
End-to-end model lifecycle management
- Training pipeline automation
- Model registry and experiment tracking
- A/B testing and canary deployments for models
- Performance monitoring and drift detection
Technical Details
Infrastructure We Build
- GPU clusters — NVIDIA A100, H100, RTX series on K8s
- Model serving — vLLM, TGI, Triton Inference Server
- Storage — High-throughput NVMe for model weights and datasets
- Networking — InfiniBand / RoCE for multi-node training
Tools & Frameworks
- Orchestration — Kubernetes with GPU operator and device plugins
- MLOps — MLflow, Weights & Biases, custom pipelines
- Monitoring — DCGM exporter, Grafana GPU dashboards
- IaC — Terraform and Ansible for reproducible GPU environments
What You Get
Assessment
We evaluate your AI use cases, data requirements, and infrastructure to design the right GPU setup.
Build
We provision GPU clusters, deploy model serving infrastructure, and set up MLOps pipelines.
Deploy
We deploy your models, configure APIs, and run performance benchmarks to ensure production readiness.
Operate
Ongoing monitoring, model updates, GPU optimization, and 24/7 support.
Why Self-hosted?
Data Privacy & Compliance
Your data never leaves your infrastructure. Full compliance with local regulations.
Zero-latency Inference
No network hops to external APIs. Inference runs on your own hardware.
Full Control over Models
Fine-tune, version, and deploy custom models without vendor lock-in.
Cost Predictability at Scale
Fixed infrastructure costs instead of unpredictable per-token API pricing.
How to Get Started
Hybrid pricing: T&M setup followed by a monthly subscription for ongoing management and support.
Frequently Asked Questions
We deploy any open-source model — LLaMA, Mistral, Falcon, Qwen, DeepSeek, and others. We also support fine-tuned and custom models.
Not necessarily. We can deploy on your existing hardware, provision cloud GPU instances (AWS, GCP, Lambda Labs), or help you source on-prem GPU servers.
Self-hosted gives you full data privacy, no per-token costs, and the ability to fine-tune models. At scale, it's significantly cheaper than API-based solutions.
Yes. We set up fine-tuning pipelines using LoRA, QLoRA, or full fine-tuning depending on your data size and requirements. Your data stays on your infrastructure.
A basic deployment takes 1–2 weeks. Full MLOps setup with training pipelines and monitoring typically takes 3–4 weeks.
Contact us for more information
Reach out to us through an email or a phone call
Or book a call to get all your questions answered
Or book a call to get all your questions answered