Ollama vs vLLM: Which One Should You Use to Run LLMs in Production?
A detailed comparison of Ollama and vLLM for serving large language models, covering throughput, memory usage, and setup.
If you are running models locally on your laptop, Ollama is the undisputed king of simplicity. But when it's time to deploy your AI application to production, you face a critical question: should you stick with Ollama or transition to a high-throughput engine like vLLM? Let's compare their throughput, memory management, and deployment complexity.
Ollama: The Developer's Best Friend
Ollama is optimized for ease of use, single-user workloads, and local system integration. It packages model weights, quantization, and server engines into a single binary. However, it lacks advanced server-side optimizations like PagedAttention or dynamic batching, making it inefficient for high-concurrency production API endpoints.
Figure 1: High-fidelity conceptual render analyzing Ollama vs vLLM: Which One Should You Use to Run LLMs in Production?.
vLLM: The Production Heavyweight
vLLM is a high-throughput LLM serving engine powered by PagedAttention, which optimizes GPU memory allocation to prevent fragmentation. It can increase query throughput by up to 2-4x compared to basic runners. If your app expects multiple concurrent users, vLLM is the industry standard.
Electromagnetic Wave Propagation & CSI Physics
To fully grasp how wireless sensing works, we must investigate the mathematical principles of modern radio frequency (RF) propagation. Traditional signals like RSSI only provide the average overall power of a received wireless packet. Conversely, Channel State Information (CSI) extracts complex vectors mapping individual Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier channels. In a standard 20 MHz or 40 MHz WiFi spectrum, the signal is split into 56 to 114 separate subcarrier channels. For each subcarrier, the CSI packet header records the exact Amplitude (signal attenuation) and Phase (fractional cycle shift).
Human bodies are comprised of more than 60% water, making them highly conductive dielectric objects in the path of 2.4 GHz and 5.8 GHz frequencies. As waves travel between the transmitter and receiver, they bounce off walls, obstacles, and humans in a phenomenon known as Multipath Propagation. The physical displacement of a human body perturbs this multipath beam network, creating constructive and destructive interference waves. For a comprehensive overview of how these physical shifts are visualized in real-time, try our Interactive 3D WiFi Radar Demo.
Selecting and Configuring ESP32 Microcontrollers
Implementing a spatial WiFi radar does not require industrial SDR (Software Defined Radio) equipment. The RuView project operates entirely on standard, inexpensive microcontrollers. For high-fidelity telemetry, we highly recommend the ESP32-S3 DevKit. The S3 series features dual XTensa LX7 cores with custom vector instruction extensions that provide hardware acceleration for raw signal matrices.
A typical DIY radar setup consists of a transmitter (Tx) emitting beacon packets and a receiver (Rx) listening on the same WiFi channel. During selection, look for boards featuring an external IPEX antenna connector instead of a standard PCB trace antenna, as high-gain external antennas heavily minimize noise. For a full list of certified microcontrollers and specific command line flashing commands, read our extensive ESP32 WiFi Radar Guide.
Figure 2: Technological block diagram demonstrating Selecting and Configuring ESP32 Microcontrollers.
Privacy Preserving Spatial Sensing & Surveillance Alternatives
As ambient computing spreads, security systems raise massive privacy concerns. Cameras record actual visual images, creating permanent files that are vulnerable to hacks. Passive WiFi sensing is **100% privacy-preserving**. It captures no optical features, faces, or bodies — only numeric signal amplitude vectors.
The data is entirely ephemeral: processed locally and instantly discarded. It is impossible to reconstruct a face from a CSI matrix. This makes WiFi sensing ideal for bedrooms, bathrooms, and private offices. For a comprehensive introduction to camera-free spatial computing, explore our starter overview What is RuView? Complete Beginner Guide.
FAQ
Can I use Ollama in production for low traffic apps?
Yes! If you only expect a few users at a time, Ollama's simple setup is perfectly fine. But for commercial applications, vLLM is recommended.
What is PagedAttention in vLLM?
PagedAttention is a memory management technique that partitions Key-Value (KV) cache into pages, preventing VRAM waste and enabling higher token concurrency.