With local AI tools exploding in popularity and enterprises racing to productionize generative AI, the choice of an LLM serving framework has become a pivotal architectural decision. While it might be tempting to use the same framework for both development and production, this approach can often lead to unforeseen challenges. This article explores two prominent open source tools, Ollama and vLLM, which cater to different ends of the LLM deployment spectrum: Ollama for accessible local prototyping and vLLM for high-performance, scalable inference. Understanding their distinct strengths is key to selecting the optimal tool for your specific workflow and deployment needs.
Ollama: A lightweight tool for local development
Ollama is designed to make running large language models as simple as possible on your local machine. It abstracts away much of the complexity associated with model setup, system dependencies, and hardware configuration. Developers can use a single command to pull and run a model, such as ollama run llama3.
This simplicity makes Ollama ideal for early-stage exploration and personal use cases. It supports running LLMs on laptops or workstations, with or without a GPU. It is well-suited for local application development. Ollama enables rapid iteration on prompts or prototype applications without requiring the management of serving infrastructure.
By reducing the setup overhead, Ollama allows developers to focus on experimenting with models like Llama 2 and Llama 3 in self-contained environments. While its performance is sufficient for low-volume usage, Ollama is not intended for high-concurrency workloads or optimized inference pipelines, typically found in enterprise-scale use cases.
vLLM: Optimized LLM serving at scale
For teams looking to deploy LLMs in production, performance, scalability, and efficiency are top priorities. vLLM is an open-source inference engine designed to meet these requirements. It provides an optimized backend for serving transformer-based models with high throughput and low latency. Developers can use a single command to pull and run a model, such as vllm serve meta-llama/Llama-3.1-8B-Instruct.
vLLM introduces several technical innovations:
- PagedAttention: Enables efficient GPU memory management across long sequences.
- Continuous batching: Allows incoming requests to be dynamically merged into active batches.
- Tensor parallelism: Supports splitting large models across multiple GPUs.
- Quantization support: Includes INT4, INT8, and FP8 formats, reducing memory footprint and improving inference speed.
These features make vLLM suitable for a range of demanding use cases:
- Production deployments where models serve hundreds or thousands of concurrent users.
- Enterprise applications such as customer support assistants, document summarization, and knowledge retrieval.
- Cost-sensitive environments where model compression and hardware efficiency are critical.
- OpenAI-compatible API for compatibility with proprietary served models.
As an example, a team deploying a Llama-3-70B model using vLLM can leverage quantization to reduce GPU memory usage while maintaining strong accuracy. Using continuous batching, they can improve utilization across multi-GPU nodes, reducing inference latency and infrastructure costs.
Choosing the right tool for your journey
Ollama and vLLM are designed to solve different problems in the LLM development lifecycle. Ollama makes it easy for developers to get started with local model experimentation, while vLLM provides a path to reliable, efficient, and scalable deployment.
Choosing the right serving solution depends on where you are in your LLM journey. For those looking to deploy models in production, vLLM offers a powerful foundation aligned with Red Hat’s open hybrid cloud vision.
Here is a comparison summarizing their key differences:
Feature | Ollama | vLLM |
Setup | One command, easy | One command, easy. Also has a lot of potential for tuning. |
Target user | Individual developers | ML engineers, production teams |
Performance | Moderate | Very high (optimized batching) |
Model scale | Small to medium (8B–13B) | Small to very large (70B+) |
Hardware | Local GPU/CPU | Any hardware accelerator |
Use case | Prototyping, testing | Scalable APIs, real-time apps |
Quantization support | Limited (weight only quantization) | Extensive: INT8, FP8, INT4, FP4 (weight, activation, attention quantization) |
Transition from local development to serving LLMs at scale with a repository of compressed, ready-to-deploy models on vLLM
As the leading commercial contributor to vLLM, Red Hat has built a curated, open source repository of quantized models optimized for vLLM, available on the Red Hat AI Hugging Face page. Originally initiated by Neural Magic and now expanded under Red Hat following its acquisition, this repository enables faster, more efficient inference at scale. The models are quantized using the open source LLM Compressor, and vLLM users can consider LLM Compressor to quantize their own fine-tuned models for production deployment running on vLLM.
Whether your focus is running a local LLM on your laptop or serving models at enterprise scale, open source offers powerful paths forward with Ollama and vLLM.
Next, we'll put these two inference engines to the test in a head-to-head performance benchmark. Find part 2 here: Ollama vs. vLLM: A deep dive into performance benchmarking
Last updated: August 11, 2025