Migration from LLMs to SLMs

The future of AI isn’t just large, it’s efficient. As enterprise needs shift toward faster, lighter, and more cost-effective deployments, we help you transition from heavyweight large language models (LLMs) to high-performance small language models (SLMs) like Microsoft’s PHI-3 series. SLMs offer comparable accuracy with significantly lower compute requirements, making them ideal for customer support, analytics, mobile apps, and regulated environments. Our migration service is built to reduce latency, cut operational costs, and give you greater control, without compromising intelligence.

What You Get

• Assessment & strategy: We evaluate your use cases and identify where SLMs deliver equivalent outcomes to LLMs, faster, cheaper, and more sustainably
• PHI‑3 model selection: Choose from AZURE’s PHI‑3‑mini (3.8B), PHI‑3‑small (7B), PHI‑3‑medium (14B), or PHI‑3‑vision (multimodal, 4.2B)
• Fine-tuning & instruction alignment: Use parameter-efficient methods (LoRA, SFT) to align SLMs with your domain, for chat, summarization, or knowledge retrieval
• Hybrid deployment architecture: Offload non-critical tasks to SLMs while retaining LLMs for deep-reasoning endpoints, and cost-optimize your AI stack
• Edge readiness & speed: Deploy on-device or in low-bandwidth environments using ONNX‑optimized, sub‑30B-parameter models
• Governance & compliance: Reduce data leakage risk, lower compute footprint, and enable on-prem/private-cloud deployment for regulated workflows

Built with the Right Stack

• SLM Models
  PHI‑3 (Mini, Small, Medium, Vision), industry-leading performance in language, code, and reasoning. Tuned for production, proven across benchmarks.
• Tuning Frameworks
  LoRA, Hugging Face Accelerate, DeepSpeed, and Azure ML pipelines, for lightweight, scalable fine-tuning that fits your domain and budget.
• Deployment Targets
  Azure AI Studio, Hugging Face, Ollama, ONNX + DirectML, NVIDIA NIM, from GPU clusters to mobile and edge, we deploy wherever your users are.
• Hybrid Orchestration
  LangChain, LangGraph, and function-calling logic to escalate from SLMs to LLMs only when necessary; faster, cheaper, smarter.
• Monitoring & Optimization
  Prometheus, Grafana, Azure Monitor, and custom token cost dashboards, so you stay in control of performance, usage, and ROI.

Hosting & Delivery

Whether you’re deploying in the cloud, at the edge, or in air-gapped systems, we build an infrastructure that suits your risk tolerance and performance goals.

• Cloud-Native: Azure Foundry for PHI‑3, AWS Bedrock for fallback LLMs

   

• Edge/On-Prem: ONNX-optimized models for fast, private inference

   

• Low-Carbon: Reduce compute impact by up to 90%, without sacrificing speed

   

• Delivery: Embedded assistants, summarization tools, hybrid pipelines, dynamic APIs, all tailored to your workflow.

Who It’s For

• Enterprise teams aiming to reduce AI spend without compromising performance, especially in customer service, analytics, and internal automation.
• Product teams building fast, lightweight AI into mobile apps, embedded systems, or offline tools.
• Regulated industries needing full control over inference, data privacy, and deployment location (on-prem, VPC, or edge).
• Startups and scaleups looking to ship faster, cheaper AI features without GPU-heavy infrastructure.
• SaaS platforms optimizing for low-latency, cost-efficient AI at scale, with dynamic model switching and hybrid orchestration.

Ready to cut costs without cutting corners? Let’s migrate your AI stack to smarter, leaner models, built for scale, speed, and sustainability.