igllama vs Ollama: Choosing Your Local LLM Runner

Both igllama and Ollama aim to make running large language models locally accessible, but they take fundamentally different approaches.

What is GGUF? 📜

GGUF (Georgi Gerganov Unified Format) is the model format used by both tools. Named after Georgi Gerganov, the creator of llama.cpp, GGUF is optimized for fast loading and inference. It’s not related to GPT - it’s purely a llama.cpp ecosystem format.

Core Philosophy 🎯

The Ollama Way

Ollama prioritizes ease of use and accessibility. It’s designed to “just work” with minimal configuration. You install it, pull a model, and start chatting. The tradeoff is less transparency and control over what happens under the hood.

igllama

igllama is built for developers who want explicit control and zero hidden complexity. Written entirely in Zig, it gives you full visibility into memory management, build processes, and runtime behavior. It’s for those who prefer clarity over convenience.

igllama 🦄

Technical Differences

Memory Management

igllama uses Zig’s explicit memory allocators. You know exactly when memory is allocated and freed. This means predictable performance and the ability to fine-tune for your hardware.

Ollama relies on Go’s garbage collector, which introduces occasional pauses and less predictable memory usage patterns. For most users this is fine, but power users may notice the overhead.

Build and Deployment

With igllama, you build from source using Zig’s build system:

zig build -Drelease-fast

This gives you a single static binary with no runtime dependencies. You can verify every byte, modify the build flags, and understand exactly what you’re running.

Ollama provides pre-built binaries that bundle dependencies. This is faster to get started but means trusting the build process and accepting larger download sizes.

Dependencies

igllama has zero runtime dependencies. The Zig standard library and llama.cpp are statically linked. No Python, no pip, no virtual environments.

Ollama requires Python for certain operations and model conversions. This adds complexity to deployment and introduces potential version conflicts.

When to Choose igllama

• You’re a Zig developer or want to learn Zig
• You need full control over memory and performance
• You prefer explicit over implicit behavior
• You want minimal, auditable code
• You’re building embedded or resource-constrained systems
• You value build reproducibility and static linking

When to Choose Ollama

• You want the simplest possible setup
• You’re okay with less transparency
• You need broad model compatibility out of the box
• You prefer Python ecosystem integration
• You don’t want to compile from source

The Bottom Line 🏁

Ollama is excellent for users who want a frictionless experience. It’s well-maintained, widely adopted, and gets the job done with minimal effort.

igllama exists for a different audience: developers who want to understand and control every aspect of their LLM runtime. It’s for those who believe software should be transparent, auditable, and free from hidden dependencies.

Both tools respect the GGUF format and contribute to the broader goal of accessible local AI. The choice depends on your priorities: convenience or control.

Getting Started

If you’re ready to try igllama, check out the installation guide and start exploring local LLMs with full visibility into what’s happening under the hood.