--- title: "Llama & Open-Weight LLM Hosting: Kensink Labs" description: "Self-hosted Llama and open-weight models from a senior lab: deployment, tuning, eval-tested, data residency. Full source ownership." source: "https://www.kensink.com/models/llama/" canonical: "https://www.kensink.com/models/llama/" --- ★ Llama LLM Models 8-week engagement META LLAMA · OPEN-WEIGHT # Self-hosted Llama, by senior engineers. GPU sizing, vLLM and Triton serving, eval pipelines. Meta's open-weight Llama models run on your own infrastructure. We deploy and tune them when data residency, cost at scale, or control demand it. Frontier LLM providers LLM API Eval pipelines [Start a conversation →](https://www.kensink.com/contact) [All llm models →](https://www.kensink.com/models) Cycle 8 weeks · fixed price Stack Llama, self-hosted Output Production code + eval suite Handoff Full source ownership \[THE SHORT VERSION\] ## Control and privacy, at the cost of running it yourself. Open-weight models like Llama let you keep data in-house, avoid per-token vendor pricing at scale, and customize freely. The trade is that you now operate inference: GPUs, serving, scaling, and updates. We help decide if that trade pays off, then run it properly. When it fits - Strict data residency or privacy requirements - High-volume workloads where hosted per-token cost hurts - Customization or fine-tuning on your own data When it does not - Low-volume needs better served by a hosted API \[HOW WE BUILD IT\] ## How we build with Llama. 01 ### Scope and fit We decide where Llama earns its place in your system, and where a simpler tool wins. No resume-driven architecture. 02 ### Build on a tested foundation We integrate Llama against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface. 03 ### Eval before launch An eval suite proves the build behaves before it reaches a user. We measure, then ship. 04 ### Handoff with ownership Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework. \[ WHAT YOU GET \] ## What the engagement leaves behind. Senior Engineers who have shipped this before 100% Source ownership at handoff Eval-first Tested before it ships 0 Framework lock-in \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fllama%2F&text=Llama%20%C2%B7%20LLM%20Models)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fllama%2F) [View .md](https://www.kensink.com/models/llama.md) \[RELATED\] ## Worth a look next. [ LLM Models Mistral Read more ](https://www.kensink.com/models/mistral)[ LLM Models DeepSeek Read more ](https://www.kensink.com/models/deepseek)[ LLM Models Claude Read more ](https://www.kensink.com/models/claude) APPLIED K-FRAMEWORK ## Bring the problem. We’ll bring the build. Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work. [Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)