We build AI applications powered by Ollama — running large language models locally on your hardware with zero data leaving your infrastructure. From privacy-sensitive AI assistants and offline-capable tools to cost-optimized inference and air-gapped deployments, Ollama makes self-hosted AI practical and performant.
Ollama is the simplest way to run open-source LLMs (Llama 3, Mistral, Gemma, Phi, Qwen) locally on Mac/Linux/Windows with a one-command install and OpenAI-compatible API. Best for privacy-first and air-gapped deployment.
We build AI applications powered by Ollama — running large language models locally on your hardware with zero data leaving your infrastructure. From privacy-sensitive AI assistants and offline-capable tools to cost-optimized inference and air-gapped deployments, Ollama makes self-hosted AI practical and performant.
Key capabilities and advantages that make Ollama Local LLM Development the right choice for your project
Run Llama 3, Mistral, Phi, Gemma, and dozens more models locally with a single command. No API keys, no internet dependency, no per-token costs.
All inference happens on your hardware — prompts, responses, and data never leave your network. Essential for healthcare, legal, financial, and government applications.
Create custom Modelfiles that package base models with system prompts, parameters, and adapters. Deploy consistent model configurations across your organization.
Ollama exposes an OpenAI-compatible REST API — your existing code works with local models by changing a single endpoint URL. Zero application rewrites needed.
Discover how Ollama Local LLM Development can transform your business
Build AI assistants for healthcare, legal, and financial domains where data cannot leave your infrastructure — with full HIPAA, SOC 2, and regulatory compliance.
Replace $50K+/month API bills with self-hosted inference on your existing GPU infrastructure — running the same quality models at a fraction of the cost.
Build local AI tools for your development team — code assistants, documentation generators, and testing tools that work offline and keep code private.
Real numbers that demonstrate the power of Ollama Local LLM Development
GitHub Stars
One of the fastest-growing open-source AI projects
+200% YoY
Supported Models
Models available in the Ollama library
+40 annually
API Compatibility
Drop-in replacement for OpenAI API calls
Full compatibility
Our proven approach to delivering successful Ollama Local LLM Development projects
Evaluate your AI needs, hardware capabilities, and privacy requirements to select the right models and deployment architecture.
Set up Ollama on your servers or cloud GPU instances with proper networking, security, and model management.
Integrate Ollama's API into your applications — chat interfaces, API endpoints, batch processing, and workflow automation.
Optimize inference performance, configure model quantization, set up monitoring, and scale across multiple GPU nodes if needed.
Find answers to common questions about Ollama Local LLM Development
For 7B models: 8GB+ RAM (CPU) or any modern GPU with 6GB+ VRAM. For 13B models: 16GB+ RAM or GPU with 10GB+ VRAM. For 70B models: 64GB+ RAM or multiple GPUs. Apple Silicon Macs run models efficiently with unified memory.
Let's discuss how we can help you achieve your goals
When each option wins, what it costs, and its biggest gotcha.
| Alternative | Best For | Cost Signal | Biggest Gotcha |
|---|---|---|---|
| vLLM | High-throughput production inference on GPUs | Free OSS; GPU infra only | More ops complexity; not ideal for laptops/dev |
| LM Studio | Desktop UI for running local models | Free | GUI-first; weaker server/API story than Ollama |
| llama.cpp | Lowest-level control, embed in native apps | Free OSS | More setup; no built-in model registry |
| Managed APIs (Together, Groq, Fireworks) | Open-source models with no ops | $0.10-0.90/M tokens | Data leaves your infra; per-token fees at scale |
Ollama infra (indicative): M2 Max Mac Studio $3K one-time runs 13B models comfortably. NVIDIA 4090 workstation ~$2-3K runs 8-13B fast; A100/H100 servers $10-40K or $1-4/hr cloud. Typical Llama 3 8B throughput: ~30-60 tok/s on 4090, ~80-150 tok/s on A100. Break-even vs OpenAI GPT-4o-mini ($0.15/$0.60 per M tokens): Ollama on $3K hardware pays back at ~20-50M tokens/mo (modest usage). Vs GPT-4o ($2.50/$10): pays back at ~3-8M tokens/mo. Plus zero data-egress concerns.
Specific production failures that have tripped up real teams.
A 13B model at fp16 uses ~26GB; add 8K context and you need 30GB+. Use quantized (Q4/Q5) variants or context-reduced configs.
Single-request-at-a-time is Ollama's default; for concurrency configure OLLAMA_NUM_PARALLEL or deploy vLLM instead.
Q4_K_M passes benchmarks on some models but fails edge cases on others—always eval on your production prompts post-quantization.
Some function-calling behaviors, streaming edge cases, and error shapes differ—test client code against real Ollama responses, not just docs.
70B models are 40-140GB; plan bandwidth and disk. Share model cache across workers via volume mounts to avoid re-downloads.
We say this out loud because lying to close a lead always backfires.
CPU-only inference on 13B+ models is painfully slow; use managed APIs or invest in proper hardware.
Top open-source (Llama 3.3 70B) approaches GPT-4 on some tasks but still trails on complex reasoning; match model to task.
Ollama is dev-friendly but not tuned for heavy prod; use vLLM or TGI for serving at scale.
Local inference on consumer GPUs typically 500ms-2s TTFT for 7-13B models; use Groq or specialized hardware for sub-200ms.