Inference

This section covers inference strategies, optimization techniques, and deployment considerations for AI models—particularly those that can run locally on consumer hardware.

WARNING

While Ollama excels at simplifying local LLM setup, be aware of several significant challenges. It demands substantial hardware (high RAM and powerful GPUs) and is prone to stability issues like service crashes, memory leaks, and inconsistent performance. Users should also expect the following:

• Manual Configuration: Default settings, such as context length, are often suboptimal and require manual adjustments for good performance.
• Limited Troubleshooting: Documentation is often sparse, and cryptic error messages make diagnosing problems difficult without community support.
• Production Unsuitability: The tool is designed for development and experimentation, not high-throughput production workloads.
• Misleading Naming Practices: Some models were mislabeled in the past to create additional hype and gain attention (e.g. Deepseek R1 Distills (4b, 7b, 8b, etc.) being advertised as the fully capable Deepseek-R1 671B)

Local Inference Engines

Running models locally offers privacy, reduced latency, and offline capabilities. Below is a quick guide to popular open-source inference engines and when you might choose each.

llama.cpp

• Best for: Flexible local inference across CPU-only, GPU-only, or hybrid CPU+GPU setups.
• Pros: Fully open source, supports a wide range of architectures (x86, ARM, Apple Silicon), and offers multiple backends including CUDA (NVIDIA), Metal (Apple) and Vulkan,. Excellent support for quantized GGUF models, enabling efficient inference even on modest hardware.
• Use when: You want maximum control, portability, and performance across diverse hardware—laptops, desktops, servers, or mobile devices.

LM Studio

• Best for: Desktop GUI users who want to explore and chat with local models.
• Pros: Intuitive interface, model discovery, built-in chat and local server mode.
• Note: LM Studio is built on top of llama.cpp for Windows/Linux and MLX (Apple's machine learning framework) on macOS, giving it strong performance and compatibility with GGUF models while providing a polished user experience.

vLLM

• Best for: High-throughput GPU inference (not ideal for low-resource devices).
• Pros: Extremely fast, supports continuous batching and PagedAttention.
• Use when: You're deploying on a server with one or more powerful NVIDIA GPUs and need scalability and low latency for many concurrent requests.

Quick Decision Guide

Scenario	Recommended Engine(s)
CPU-only or mixed CPU/GPU inference	llama.cpp
Desktop GUI for chatting/testing	LM Studio
Server deployment with NVIDIA GPU(s)	vLLM

💡 Tip: Most local LLMs today use quantized versions (e.g., GGUF, AWQ, GPTQ) to reduce memory usage and run on consumer hardware. Always check model compatibility with your chosen engine.

For up-to-date benchmarks and model support, consult each project's documentation and community resources.