I've been benchmarking the two main tools for running LLMs on Raspberry Pi, and I want to document what I'm finding because the trade-off between them isn't obvious.
The Setup
Raspberry Pi 5, 4GB RAM, Raspberry Pi OS (64-bit)
Model: TinyLlama 1.1B Q4_K_M
Test: 100 token generation, measured 10 times, averaged
Llama.cpp Results
Tokens per second: 8.2 ± 0.4
Peak RAM: 890MB
Model load time: 2.9 seconds
Total setup time: 2.5 hours
Installation steps: 12 (clone, compile, configure)
To get these numbers, I had to:
Clone the repository
Install build tools (gcc, g++, make)
Compile from source with ARM NEON flags
Test different thread counts to find optimal (4 threads on 4-core Pi)
Measure model load time multiple times to eliminate variance
Run benchmark 10 times and average
Each step had potential failure points. The compile step took 45 minutes on the Pi.
Ollama Results (Default Settings)
Tokens per second: 5.7 ± 0.3
Peak RAM: 1.1GB
Model load time: 5.4 seconds
Total setup time: 8 minutes
Installation steps: 1 (curl bash)
Installation literally took 8 minutes. Open terminal, paste one command, wait.
The problem: These numbers don't represent the actual capability of the Pi. They represent Ollama's default configuration on a Pi, which isn't optimal.
Ollama Results (Optimized Settings)
After manually setting OLLAMA_CONTEXT_LENGTH=512:
Tokens per second: 7.2 ± 0.3
Peak RAM: 890MB
Model load time: 4.2 seconds
Total setup time: 12 minutes (8 min install + 4 min config)
Installation steps: 2 (install + set env variable)
Same hardware. Same model. One environment variable changed. 26 percent performance improvement.
The Trade-off
Llama.cpp:
Pros: Fastest performance (8.2 tokens/sec), lowest RAM (890MB), actively optimized
Cons: 2.5 hour setup, requires technical knowledge, steep learning curve
Ollama:
Pros: 8 minute setup, zero technical knowledge required, user-friendly
Cons: 5.7 tokens/sec by default (you lose 30 percent), doesn't auto-optimize, requires knowledge of environment variables to fix
The Unspoken Problem
Most users encounter Ollama first because it's easier. They get 5.7 tokens/sec. They think the Pi is slow. They don't know that with one configuration change they'd get 7.2 tokens/sec.
Some users dig into llama.cpp. They get 8.2 tokens/sec. But they had to spend 2.5 hours learning compile flags and ARM architecture.
Neither experience is designed for someone who just wants to run AI locally on their Pi.
What Developers Need
Automatic hardware detection. Detect "this is a Pi" and optimize accordingly.
Sensible defaults for the hardware. Not x86 defaults on ARM hardware.
Clear setup path. "From zero to running inference" should be minutes, not hours.
Real-time visibility. Show me what's actually happening (RAM usage, CPU load, temperature).
Honest benchmarking. Let me know if my setup is actually optimal.
What's Coming
The Pi community is growing. The demand for local AI on Pi is real. The gap between what's technically possible (8.2 tokens/sec) and what's practically accessible (8 minute setup) is being noticed.
Some people are working on closing that gap.
In the next few weeks, new tools will ship that attempt to combine the speed of llama.cpp with the accessibility of Ollama.
The interesting part is watching which approach wins and why.
Top comments (0)