Your data never leaves your infrastructure
Deploy Verbalyze-optimized Indic LLMs on your private cloud, on-premise servers, or air-gapped environment. Full data sovereignty. Zero external API calls. DPDP and RBI compliant from day one.
Enterprise-grade on-premise AI infrastructure
Everything you need to run Indic LLMs in production — inside your own network boundary.
Absolute Data Sovereignty
Your audio, transcripts, and LLM inference never cross your network boundary. Ideal for government, defence, banking, and healthcare where data residency is a regulatory requirement — not a preference.
INT4 & INT8 Quantization
Verbalyze-optimized quantized weights run Llama 3 Indic and Gemma-Indic at INT4 precision — 4× smaller memory footprint, 2× faster inference — with less than 1% accuracy degradation on Indian language benchmarks.
ONNX Runtime Optimized
Model weights exported to ONNX format and optimized with ONNX Runtime for maximum throughput on NVIDIA CUDA, AMD ROCm, and Intel OpenVINO. GPU-vendor agnostic deployment.
Docker & Kubernetes Native
Production-ready Docker containers and Kubernetes Helm charts. Deploy to your existing GCP, AWS, Azure, or on-premise Kubernetes cluster. HPA auto-scaling based on request queue depth.
Indic-Fine-Tuned Models
Base models fine-tuned on curated Indian language corpora — BFSI terminology, healthcare vocabulary, government language, and conversational Indic text. Outperform generic LLMs by 20–40% on Indian domain tasks.
Air-Gap Support
Full offline deployment with no outbound internet dependency. Model weights delivered via encrypted USB or private S3 mirror. Updates delivered via signed model packages — no external API calls required.
Inference Observability
Built-in Prometheus metrics, Grafana dashboards, and OpenTelemetry traces. Monitor token throughput, GPU utilization, queue depth, P95/P99 latency, and error rates out of the box.
Model Security & Integrity
Signed model weights with SHA-256 checksums. License enforcement via hardware fingerprinting. Model weights are encrypted at rest using AES-256. Prevents unauthorized copying or redistribution.
Available Indic LLM Models
All models are Verbalyze fine-tuned on Indian language data. GPU specs are minimum recommended.
| Model | Base | Precision | Min GPU | Throughput | Languages |
|---|---|---|---|---|---|
| Llama 3 Indic 8B | Meta Llama 3 8B | INT4 / INT8 / FP16 | 1× H100 80GB | 185 tok/s | 30+ Indian languages |
| Llama 3 Indic 70B | Meta Llama 3 70B | INT4 / INT8 / FP16 | 1× H200 141GB | 85 tok/s | 30+ Indian languages |
| Gemma 2 Indic 9B | Google Gemma 2 9B | INT4 / INT8 / FP16 | 1× H100 80GB | 210 tok/s | 25+ Indian languages |
| Qwen 2.5 Indic 14B | Alibaba Qwen 2.5 14B | INT4 / INT8 / FP16 | 1× H100 80GB | 145 tok/s | 30+ Indian languages |
| Mistral Indic 7B | Mistral 7B | INT4 / INT8 / FP16 | 1× H100 80GB | 240 tok/s | 15+ Indian languages |
| DeepSeek Indic 7B | DeepSeek-V3-Base 7B | INT4 / INT8 / FP16 | 1× H100 80GB | 220 tok/s | 20+ Indian languages |
| Kimi Indic 8B | Moonshot Kimi 8B | INT4 / INT8 / FP16 | 1× H100 80GB | 170 tok/s | 15+ Indian languages |
From zero to production in 4 weeks
Our deployment engineering team handles every step — you focus on your use case.
Requirements Assessment
We assess your use case, data volume, GPU inventory, and compliance requirements. 2-hour workshop.
Model Selection & Sizing
Select the right model family, precision level, and hardware configuration for your latency and throughput targets.
Infrastructure Provisioning
We provide Terraform templates for cloud or on-prem setup. Our team handles GPU driver, CUDA, and ONNX runtime configuration.
Model Deployment
Encrypted model weights delivered and deployed via Helm chart. Load balancer, autoscaler, and monitoring configured.
Fine-Tuning (Optional)
Domain-specific fine-tuning on your proprietary data — BFSI, healthcare, legal. Done inside your infrastructure. Data never leaves.
Ongoing Support
Quarterly model updates, performance reviews, and 24×7 L2 support from Verbalyze's deployment engineering team.
Sovereign AI for data-sensitive industries
Private Banking AI Assistant
A sovereign LLM that answers complex financial queries, drafts loan summaries, and generates compliance reports — entirely within the bank's internal network. No customer data touches external APIs.
Clinical Decision Support
On-premise LLM trained on Indian clinical guidelines, ICD-10 codes, and AYUSH protocols. Helps doctors draft prescriptions, discharge summaries, and referral letters in local languages.
Legal Document Intelligence
Contract review, clause extraction, and case summarisation in Hindi and English. Deployed inside law firm or government legal department networks — confidential documents never leave.
Government & Defence
Air-gapped deployment for classified environments. Handles Hindi and regional language document processing, translation, and summarisation for internal government workflows.
When cloud AI isn't enough
Frequently Asked Questions
What is data egress and how does self-hosting prevent it?
Data egress refers to data leaving your private network. By deploying our Indic LLMs on your own private cloud or physical servers, all speech processing and text inference happen inside your perimeter. No voice recordings or text summaries are sent to external APIs.
What hardware is required to host the models?
For developer tests, a single consumer GPU like an NVIDIA RTX 4090 is sufficient. For production high-throughput workloads, we recommend enterprise GPUs such as NVIDIA A100 or H100. We support INT4 and INT8 quantization to optimize GPU memory footprint.
Do you support completely air-gapped deployments?
Yes. For highly secure or defence environments, we support fully offline deployments. Model weights and software updates are delivered via secure physical media or signed package repositories with no external internet requirements.
Are the self-hosted models customisable?
Yes. You can fine-tune our models on your own domain-specific data (e.g. internal customer logs, banking product databases) directly within your own secure environment, ensuring your training data is kept private.
How are software updates handled on-premise?
We publish monthly updates containing model refinements, vocabulary additions, and security patches. These are delivered as signed Docker containers and Helm charts that can be deployed via your internal CI/CD pipelines.
Ready to deploy sovereign AI?
Talk to our deployment engineering team. We'll assess your requirements and design a reference architecture in 48 hours.