Technology
Ollama Development — Local LLM Serving for Private AI
Ship private, offline-capable AI features with Ollama — local LLM serving for desktops, edge servers, and air-gapped enterprises.
What we build with Ollama
- Local LLM serving with Ollama on macOS, Linux, and Windows
- Air-gapped enterprise deployments on-prem or in private cloud
- Model packaging and registry management for fleets of machines
- Embeddings and RAG pipelines that never call out to the internet
- Integration with Ollama’s OpenAI-compatible API for drop-in replacement
- Hybrid routing between local Ollama and cloud models when needed
Why DiveScale
Built by engineers who ship Ollama in production
Ollama is the fastest way to ship private, local-first AI features — whether for a desktop app, an internal tool that must run offline, or an enterprise that won’t let prompts leave the building. DiveScale builds end-to-end Ollama integrations from prototype through fleet deployment.
We use Ollama’s OpenAI-compatible API so the same client code targets hosted GPT in dev and local Ollama in production, when the threat model demands it. Models are pinned, versioned, and rolled out like any other dependency.
Where local hardware is the constraint, we pick the right quantized model (Llama, Mistral, Phi, Qwen) for the device class, and benchmark against your quality bar.
Ollama use cases we deliver
How we deliver
Our Ollama delivery process
- 01
Hardware + model audit
We profile the target hardware and pick the right quantized model — Llama, Mistral, Qwen, Phi — for quality and speed.
- 02
Prototype the AI feature
Working integration in 1–2 weeks using Ollama’s OpenAI-compatible API so future cloud migration is reversible.
- 03
Fleet packaging
We package Ollama plus your custom models for distribution to user machines or fleet-managed servers.
- 04
Operate & update
Model versioning, telemetry, and graceful upgrades across the fleet — the same discipline you bring to any production dependency.
Related technologies
LLaMA
Self-host Meta’s LLaMA family for private, controllable, and cost-predictable AI — on your VPC or our managed infrastructure.
Learn moreDeepseek
Production deployment of Deepseek-V3 and Deepseek-Coder for reasoning, coding, and high-volume workloads at a fraction of frontier-model cost.
Learn moreLLMs
Production LLM engineering — model selection, RAG, fine-tuning, evals, guardrails, and the operational layer that keeps quality high.
Learn moreMLOps
MLOps platform engineering — pipelines, model registries, evaluation, monitoring, and incident response for ML and LLM systems.
Learn moreOllama — Frequently Asked Questions
When data cannot leave the device or network; when usage is high and per-token cost matters; when offline operation is required; or when latency demands sub-100ms responses. Otherwise hosted APIs usually win on quality and ease.
