Can open-source models match GPT-4 or Claude 3.5 in quality?

Yes. Latest open-weight models like DeepSeek-R1/V3, Llama 3.3 70B, and Qwen 2.5 match or exceed proprietary APIs across coding, reasoning, math, and multilingual tasks. When fine-tuned on your specific company data, they often outperform generic cloud models.

What hardware is required to run open-source models?

It depends on model size and concurrency. Efficient models (8B–14B) run smoothly on a single GPU (like RTX 4090 or L4), medium models (32B–70B) run on 1 to 2 A100/H100/L40S GPUs with FP8/AWQ quantization, and full-scale reasoning models run on multi-GPU private clusters. We size the exact hardware to fit your budget.

How does private hosting reduce costs compared to cloud APIs?

With cloud APIs, high volume and long context windows result in compounding per-token fees. With self-hosted models, your hardware costs are fixed regardless of how many millions of tokens or queries your team processes.

Is our data completely safe from third parties?

Yes, 100%. The model weights and inference server run entirely inside your own private network or dedicated data center. No prompt data, user inputs, or embeddings ever leave your infrastructure.

Can you deploy in air-gapped or offline environments?

Yes. We configure completely air-gapped inference environments for defense, banking, government, and healthcare clients with zero external internet connectivity required.

How do you keep self-hosted models updated?

We set up automated CI/CD pipelines for model evaluation, weight updating, and continuous fine-tuning as new open-source model releases emerge.

AI Solutions

Own Your AI Models on Your Own Hardware

We help enterprises deploy, fine-tune and scale open-weight AI models (DeepSeek, Llama 3, Mistral, Qwen) on private servers and private clouds — ensuring 100% data privacy and eliminating expensive API token bills.

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1Overview 2Capabilities 3Process 4FAQ

Self-host, fine-tune and run state-of-the-art open-weight AI models on your private hardware — 100% data sovereignty, zero API vendor lock-in.

Open-Weight Model DeploymentProduction deployment of DeepSeek (R1, V3), Meta Llama 3.3/3.1, Mistral, Qwen 2.5, and Gemma on your private infrastructure.
High-Performance Inference EnginesHigh-throughput, low-latency serving powered by vLLM, TensorRT-LLM, SGLang, and Ollama with continuous batching and PagedAttention.
Private Domain Fine-TuningLoRA, QLoRA, and full parameter fine-tuning on your proprietary internal datasets without exposing sensitive data.
Model Quantization & OptimizationAWQ, GPTQ, FP8, and GGUF quantization to run high-intelligence models on optimized GPU clusters or edge hardware at minimal cost.

1Overview

Why companies are ditching cloud LLM APIs

Sending sensitive customer data, source code, and internal records to third-party API providers creates severe compliance and privacy risks, while per-token pricing skyrockets as your team scales. With modern open-weight models matching or exceeding proprietary APIs, hosting on your own infrastructure gives you total data sovereignty, predictable fixed compute costs, and unlimited freedom to fine-tune on proprietary domain knowledge.

2Capabilities

What we deliver

Open-Weight Model Deployment

Production deployment of DeepSeek (R1, V3), Meta Llama 3.3/3.1, Mistral, Qwen 2.5, and Gemma on your private infrastructure.

High-Performance Inference Engines

High-throughput, low-latency serving powered by vLLM, TensorRT-LLM, SGLang, and Ollama with continuous batching and PagedAttention.

Private Domain Fine-Tuning

LoRA, QLoRA, and full parameter fine-tuning on your proprietary internal datasets without exposing sensitive data.

Model Quantization & Optimization

AWQ, GPTQ, FP8, and GGUF quantization to run high-intelligence models on optimized GPU clusters or edge hardware at minimal cost.

Air-Gapped & Sovereign Security

100% offline, air-gapped deployments meeting strict HIPAA, GDPR, banking, and GCC regional data residency regulations.

Enterprise Tool & RAG Integration

Secure integration of private models with internal vector databases, ERPs, CRMs, and agentic workflows.

3Process

How we deploy your private AI

Step 1

Workload & Hardware Sizing

We audit your use case, tokens/sec requirements, and size optimal GPU servers (NVIDIA H100, A100, L40S, RTX 6000 Ada, or private cloud).

Step 2

Model Selection & Benchmarking

We benchmark candidate open-weight models (DeepSeek, Llama, Qwen, Mistral) against your specific enterprise tasks.

Step 3

Fine-Tuning & Quantization

We fine-tune the model with your company's data and quantize weights for maximum throughput and minimum VRAM usage.

Step 4

Deployment & Orchestration

We set up secure serving stacks (vLLM / Kubernetes / Ray) with load balancing, caching, and failover protection.

Step 5

Observability & Guardrails

We install real-time monitoring for latency, token throughput, hallucination guardrails, and model drift.

4FAQ

Frequently asked questions

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Serving startups and businesses across the USA, Middle East, Europe, Asia and Africa.