LLM inference engine debate: Ollama or vLLM?

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What is  Ollama and how to recognize it  ?

import subprocessdef run_ollama(model_name, prompt): """ Run a prompt against a local Ollama model. """ result = subprocess.run( ["ollama", "run", model_name], input=prompt.encode(), stdout=subprocess.PIPE, text=True ) return result.stdout# Example usageresponse = run_ollama("gpt-neo", "What are the benefits of local AI inference?")print(response)

     At the same time, this platform places particular emphasis on support for quantized models, which is crucial to significantly reduce memory usage and improve model performance. Ollama provides a growing library of pre-trained models, ranging from general multi-functional models to specialized models for specific segmentation tasks. Available models worth noting include Llama 3.1, Qwen, Mistral, and professional variants like DeepSeek-coder-v2.

     In addition, Ollama's user-friendly installation process and intuitive model management benefit from its unified Modelfile format. Its wide cross-platform support, including Windows, macOS, and Linux, further enhances its ease of use. By providing local model services with OpenAI-compatible interfaces, Ollama is undoubtedly a robust and attractive choice for developers who seek the flexibility of local deployment and want to easily integrate standard APIs.

     1. Core functions

     Ollama's core goal is to optimize the deployment process of LLMs so that users can efficiently run models on "local devices" without relying on cloud services or complex infrastructure. This localized deployment method not only improves data privacy protection, but also provides users with greater control and flexibility.

    (1) The bridge role of localized deployment

     As a bridge for LLMs deployment, Ollama simplifies the deployment process that traditionally requires high-performance computing clusters and complex configurations. Users can run models on ordinary personal computers or single-GPU devices, lowering the hardware threshold.

• Privacy and Security: By running locally, Ollama ensures that sensitive data does not leave the user's device, meeting privacy requirements in fields such as healthcare, finance, and law. For example, a healthcare organization can use Ollama to run LLaMA models to analyze patient records without uploading the data to the cloud.

• Customizable experience: Ollama allows users to adjust model parameters according to their needs, such as setting the generation temperature (Temperature) or the maximum output length (Max Length) to meet the requirements of specific tasks.

    (2) Seamless integration of OpenAI-compatible APIs

     Ollama provides an interface compatible with the OpenAI API, enabling users to seamlessly migrate existing tools and workflows to the local environment. This compatibility significantly reduces the learning cost for developers.

     Generally speaking, users can call Ollama models through REST APIs and integrate with Python, JavaScript, or other programming languages. For example, developers can use Python's requests library to send API requests to obtain text generated by the model.

      2. Technical highlights

     Ollama has demonstrated outstanding performance in performance optimization and resource management. With its support for quantitative models and efficient inference processes, it provides a lightweight operating experience, especially suitable for environments with limited resources.

   (1) Quantitative model support

     Ollama focuses on supporting quantized models, using 4-bit and 8-bit quantization technology (such as Int8 and Int4), which significantly reduces the memory usage of the model while improving inference performance.

• Quantization advantage: Taking the LLaMA-13B model as an example, it requires about 26GB of video memory when not quantized. After using Int8 quantization, the video memory requirement is greatly reduced to 7GB, greatly reducing the demand for hardware.

• Performance improvement: Quantization not only reduces video memory usage, but also effectively accelerates inference speed. For example, when running the quantized LLaMA-13B model on an NVIDIA RTX 3060 (12GB video memory), the inference speed can reach 10 tokens/s, significantly improving processing efficiency.

• Application scenarios: Thanks to quantitative support, Ollama performs well in resource-constrained environments and is particularly suitable for running on ordinary laptops, such as educational experiments, personal development, or lightweight application scenarios.

   (2) Memory Management and Inference Efficiency

     Ollama uses memory mapping technology to optimize the model loading speed, so that the startup time is usually within 30 seconds, greatly improving the user experience.

• Single-threaded reasoning: Ollama is designed with a single-threaded reasoning architecture, which simplifies the system structure and avoids the complexity and resource competition caused by multi-threading. This makes Ollama more suitable for low-concurrency scenarios and can efficiently complete reasoning tasks.

• Cross-platform support: Ollama is compatible with Windows, macOS, and Linux systems, ensuring that users can enjoy a consistent performance experience across different operating systems. For example, on macOS, users can use the neural engine of the M1/M2 chip to accelerate reasoning and further improve processing speed and efficiency.

What is  vLLM and how to recognize it  ?

import requestsdef query_vllm(api_url, model_name, prompt): """ Send a prompt to a vLLM API endpoint. """ payload = { "model": model_name, "prompt": prompt, "max_tokens": 100 } response = requests.post(f"{api_url}/generate", json=payload) return response.json()# Example usageapi_url = "http://localhost:8000"result = query_vllm(api_url, "gpt-j", "Explain the concept of throughput in AI.")print(result)

     Comparison with traditional frameworks: Compared with traditional reasoning frameworks such as Hugging Face Transformers, vLLM has significant advantages in throughput and resource utilization, and the reasoning speed can be increased by 2-4 times.

      The technical core of vLLM lies in its innovative memory management and reasoning optimization technology, which achieves efficient resource utilization and excellent reasoning performance through PagedAttention and distributed computing framework.

     1. PagedAttention technology: a breakthrough in memory management:

    Technical principle: PagedAttention stores the key-value cache (KV Cache) in blocks, which is similar to the paging memory management (Paging) in the operating system. This method reduces memory fragmentation by dynamically allocating video memory and significantly reduces video memory usage.

     Performance improvement: In traditional reasoning frameworks, KV Cache occupies a large amount of video memory, which is especially serious when reasoning with long sequences. PagedAttention reduces the amount of video memory used by 50%-70%, enabling vLLM to process larger models or longer contexts under the same hardware conditions.

     Application effect: Taking the LLaMA-13B model as an example, the traditional framework requires about 26GB of video memory in FP16 format, while vLLM only needs 10GB of video memory to run after PagedAttention optimization.

     2. Distributed reasoning and high throughput:

     Distributed computing framework: vLLM is built on PyTorch and Ray, supports multi-GPU distributed reasoning, and improves throughput through parallel computing.

     Continuous Batching: vLLM uses continuous batching technology to dynamically adjust the batch size to maximize GPU utilization. For example, when running the LLaMA-13B model on 4 NVIDIA A100 GPUs, vLLM's throughput can reach 5000 tokens/s.

     High concurrency support: vLLM can handle hundreds of concurrent requests, and the inference speed remains stable, which is suitable for high-load production environments.

     3. Resource utilization optimization:

     FP16 reasoning: vLLM uses half-precision floating point (FP16) format reasoning by default. Combined with the GPU's Tensor Core accelerated computing, the reasoning speed is more than 2 times faster than the FP32 format.

     Dynamic scheduling: vLLM has a built-in efficient request scheduler to optimize task allocation, ensure balanced resource allocation in high-concurrency scenarios, and avoid performance bottlenecks.

     Low latency: Through memory optimization and distributed computing, the inference latency of vLLM is significantly reduced, and the average response time can be controlled within 100ms.

vLLM vs  Ollama  , how to choose  ?

     To sum up, in specific business applications, for specific demand scenarios, we put forward the following selection suggestions:

     1. For scenarios that prioritize data privacy and simplified deployment: Ollama is recommended. Ollama is particularly suitable for localized, offline operations, or environments with limited computing resources. Ollama can provide convenient model deployment and management capabilities.

     2. For scenarios with high requirements for inference performance and system scalability: vLLM is recommended. It is especially suitable for applications that need to handle high concurrent requests and large-scale inference tasks. vLLM performs well in performance optimization.

    3. Comprehensive considerations and gradual adoption strategies: When choosing a framework, users should comprehensively evaluate their own technical capabilities, specific application requirements, available hardware resources, and the priority of performance and ease of use. For example, for beginners or users who want to get started quickly, they can give priority to Ollama as an introduction. After becoming familiar with the LLM reasoning process and principles, they can gradually turn to vLLM to obtain higher performance and stronger scalability according to the needs of more complex applications.

     That’s all for today’s analysis. For more in-depth analysis, best practices, and related technology frontiers about Function-Calling and MCP related technologies, please follow our WeChat public account: Architecture Station to get more exclusive technical insights!