Ollama Review

We have been running Ollama as our primary local model server for approximately six months across three workflows:

• Development: Running coding models (Qwen, DeepSeek, GLM variants) for agent development and testing without API costs
• Agent testing: Serving models behind OpenAI-compatible endpoints for agent test harnesses and benchmark runs
• Research: Quick local inference for document summarization, prompt experimentation, and model comparison

The deployment runs on Ubuntu with NVIDIA CUDA GPUs (RTX 4090 and RTX 6000 Ada) and also on an Apple Silicon Mac for portable testing. We use Ollama's CLI, its OpenAI-compatible API, and integrations with Open WebUI, Continue, and Hermes Agent.

Installation is genuinely one command. The curl | sh installer works, detects your GPU, and has you running a model in under five minutes. This is the best developer experience in the local LLM serving space, bar none. No Docker compose files, no CUDA toolkit configuration, no model format conversion — just ollama run llama3.

The model registry is excellent. ollama pull handles GGUF quantized models from a curated registry. You do not need to hunt for the right quantization or worry about format compatibility. The registry covers the major open-weight families — Llama, Qwen, DeepSeek, GLM, Mistral, Phi, and more.

OpenAI-compatible API is reliable. Point any OpenAI SDK client at http://localhost:11434/v1 and it works. This is how we run agent test suites against local models — the agent code does not change, only the base URL. Hermes Agent, LiteLLM, LangChain, and custom Python scripts all connect without issue.

Modelfiles are intuitive. Creating a custom model with a specific system prompt, parameters, and base model is a Dockerfile-like syntax that any developer can read. This makes it easy to version and share model configurations.

Apple Silicon support is first-class. On M-series Macs, Ollama uses Metal acceleration effectively. Performance is good enough for interactive development and testing, though not for production throughput.

Resource management is sane. Ollama unloads models from memory after a configurable idle timeout. This matters when you are running multiple models on a single GPU — you do not need to manually manage VRAM.

Throughput is not competitive with vLLM for production serving. Ollama is optimized for developer convenience, not high-throughput inference. If you need to serve many concurrent requests, vLLM with continuous batching will significantly outperform Ollama on the same hardware. Ollama is for development and testing, not for serving production traffic at scale.

Model selection is curated, not comprehensive. The registry covers popular models well, but if you need a specific fine-tune, an obscure quantization, or a model that was not added to the registry, you need to import a GGUF file manually. This works but is less polished than the registry flow.

No built-in quantization control. Ollama selects a default quantization for each model. If you want to choose between Q4_K_M, Q5_K_S, Q8_0, or FP16, you need to import the specific GGUF file. The defaults are reasonable but not always optimal for your specific quality/speed tradeoff.

Multi-GPU scheduling is basic. On a machine with multiple GPUs, Ollama will use them, but you do not have fine-grained control over model placement or tensor parallelism the way you do with vLLM. For single-GPU workflows this does not matter, but for large models that need multi-GPU sharding, vLLM is the better tool.

No built-in observability. Ollama gives you basic logs but no tracing, no token-level metrics, no cost tracking. For production-grade observability, you need to pair it with Langfuse or Helicone through the OpenAI-compatible API. This is not a deficiency — it is a scope decision — but it means Ollama alone is not enough for a production stack.

Memory usage on loaded models can be surprising. A 70B model in Q4 quantization uses roughly 40GB of VRAM. Ollama will load it if your GPU has the memory, but if you are also running other GPU workloads, you can get OOM errors. The idle timeout helps, but you need to be aware of what is loaded at any given time.

Ollama is the best tool for developers who want to run local LLMs without becoming ML infrastructure engineers. If your goal is to test agent workflows locally, experiment with open-weight models, or build a development environment that does not depend on API keys, Ollama is the right choice.

It is not the right choice if you are building a production inference serving platform. For that, vLLM with proper batching, tensor parallelism, and observability is the better tool. Many teams will use both — Ollama on the developer laptop, vLLM on the production server.

Ollama earns a 4.0 after six months of daily use. It loses points for throughput limitations, lack of multi-GPU control, and absent observability. It gains points for the best installation experience in the space, a reliable OpenAI-compatible API, and a model registry that removes the friction from local model experimentation. For development and testing workflows, Ollama is the default we recommend. For production serving, look elsewhere.