Running Ollama Locally: A Practical Guide for Small Teams
Local LLM inference addresses a specific set of requirements that cloud-hosted models cannot: data sovereignty, predictable costs at scale, zero-latency availability, and operation without network connectivity. For organizations in regulated industries — legal, healthcare, financial services — or those processing sensitive internal data, these properties may outweigh the capability gap between local and frontier models.
Ollama provides a runtime for local model inference with a relatively low barrier to entry. This post covers the practical considerations for deploying it in a small team environment, including hardware sizing, model selection, multi-user access, and the operational realities that documentation tends to understate.
Hardware sizing
Model inference is memory-bound. The entire model must fit in RAM (or VRAM, for GPU-accelerated inference) for acceptable performance. When available memory is insufficient, the system pages to disk and response latency increases by orders of magnitude — from seconds to minutes per response.
For 7–8B parameter models (Llama 3 8B, Mistral 7B), plan for approximately 8GB of memory dedicated to the model. On a machine with 16GB total RAM, this leaves limited headroom for the operating system and other applications. 32GB is a more comfortable baseline for sustained use alongside normal workloads.
13B parameter models require 16GB+ dedicated to inference. 70B models require 64GB+ RAM or dedicated GPU hardware with sufficient VRAM (NVIDIA A6000 or equivalent). For most small teams, 70B models are not practical without purpose-built infrastructure.
Apple Silicon Macs (M1/M2/M3/M4) with unified memory architecture offer strong price-performance for local inference. Ollama supports Metal acceleration natively, and the unified memory pool eliminates the CPU-GPU transfer overhead present in discrete GPU systems. A Mac Mini with 32GB unified memory handles 7–13B models well for individual or small team use.
Model selection
The choice of model depends on the target use case. There is no universal "best" model — each involves tradeoffs between capability, speed, and resource consumption.
For general business tasks (email drafting, document summarization, meeting notes), Llama 3 8B provides a reasonable balance of quality and speed. For code-related tasks, Mistral 7B and CodeLlama variants perform well. Microsoft's Phi-3 is worth evaluating on memory-constrained hardware. Google's Gemma 2 shows strength in structured instruction-following tasks.
We recommend deploying one model, evaluating it against actual team workflows for one to two weeks, and adjusting based on observed quality. Model switching in Ollama is a single command — there is no migration cost.
Network and multi-user configuration
By default, Ollama binds to localhost (127.0.0.1:11434). For single-user deployments on the same machine, this requires no additional configuration. For multi-user access across a local network, the bind address must be changed to 0.0.0.0 via the OLLAMA_HOST environment variable.
This introduces security considerations. Ollama does not include authentication or access control. Any device on the network can send requests to an exposed Ollama instance. For team deployments, placing Ollama behind a reverse proxy with basic authentication, or restricting network access via firewall rules, is advisable.
Integration patterns
Ollama exposes an API compatible with the OpenAI chat completions format. This compatibility means that applications built against the OpenAI API can often be pointed at a local Ollama instance with minimal modification — typically changing the base URL and removing the API key requirement.
Common integration targets include Open WebUI (a self-hosted ChatGPT-style interface), Continue.dev (VS Code AI coding extension), and custom applications using standard HTTP client libraries. The API surface is well-documented and stable across Ollama releases.
Operational considerations
Local LLM deployment introduces ongoing operational responsibilities that cloud-hosted APIs abstract away: model updates, Ollama runtime updates, hardware monitoring, and capacity planning as usage grows. Someone on the team needs to own this maintenance surface.
Response quality from local models is measurably below frontier models (GPT-4, Claude 3.5 Sonnet) for complex reasoning, nuanced writing, and multi-step tasks. Local models are best positioned for routine, high-volume tasks where privacy and cost predictability outweigh the capability gap.
Getting help
If you're evaluating whether local LLM deployment fits your team's requirements, or need assistance with hardware selection, network configuration, and integration setup, book a free 15-minute call and we'll assess the fit together.