Meta Llama and Ollama as the self-hosted AI stack
Meta's Llama family has become the dominant open-weight LLM series — Llama 3.3 70B, Llama 4 variants, and the specialist code and math derivatives all ship under a permissive commercial license that lets enterprises self-host without per-token cost or data-leaving-the-environment concerns. Ollama has emerged as the simplest operational path for running Llama (and Qwen, Mistral, Gemma) on enterprise infrastructure — Docker-like semantics, a well-designed REST API, and a community library of optimized model packages.
How Thoughtwave integrates Llama + Ollama
Our self-hosted AI engagements use this combination as the default:
- Ollama-served models on client-owned GPU infrastructure — typically H100, H200, or MI300 depending on availability and budget.
- Llama 3.3 70B as the general-purpose reasoning model for most workloads.
- Qwen 2.5 and Gemma 27B as ensemble members where different sub-tasks benefit from different model strengths.
- vLLM for high-throughput production workloads where Ollama's simplicity is outweighed by vLLM's batching and paged-attention performance.
- MCP tool protocol for agent workloads — Llama handles tool calling well enough for most enterprise agent use cases.
The canonical reference deployment is TWSS Commercial Credit AI — a 3-model ensemble (Qwen 2.5, Gemma 27B, Llama 3.3 70B) served via Ollama, zero external API calls, full audit trail, MBE/GSA-procurement-ready.
Operational considerations
Self-hosted deployments trade vendor dependency for infrastructure operations. GPU capacity planning, model-version upgrades, and inference performance tuning are real work. Our engagements pair a client infrastructure lead with our own AI platform engineers for the first deployment; subsequent workloads on the same platform are substantially lower-effort because the operational pieces (monitoring, versioning, capacity) are in place.
When Llama + Ollama is the right default
For regulated workloads where data cannot flow to external vendors — HIPAA, GLBA, FedRAMP, specific client contract restrictions — this is the only answer that works. For high-volume workloads where token economics make self-hosting cheaper than API calls, this is the right bet. For enterprises that want vendor-independence as a design principle, Llama's open license and Ollama's operational maturity make this stack the lowest-risk long-term posture.