System Requirements
Memo runs AI models locally. The hardware you need depends on the model size and whether you use GPU acceleration.
Minimum Requirements
| Component | Minimum |
|---|---|
| RAM | 8 GB |
| Disk space | 4 GB free |
| CPU | x86-64 with AVX2 support (Intel Haswell 2013+ or AMD Excavator 2015+) |
| GPU | Not required — CPU inference works |
| Internet | Required only for model downloads and cloud providers |
With 8 GB RAM, you can run quantized models up to ~3B parameters comfortably on CPU. The smallest functional models (1–3B) need ~2–4 GB RAM for inference.
Recommended Requirements
| Component | Recommended |
|---|---|
| RAM | 16 GB |
| Disk space | 20 GB free (models range from 1–15 GB each) |
| CPU | x86-64 with AVX2, 8+ cores |
| GPU | NVIDIA with 6 GB+ VRAM (CUDA 11.7+) |
| Internet | Broadband for model downloads |
With a GPU, llama.cpp offloads layers to VRAM, dramatically increasing inference speed. A 6 GB VRAM GPU handles 7–8B parameter models at interactive speeds.
Operating Systems
| OS | Version | Status |
|---|---|---|
| Windows | 10, 11 (x86-64) | ✅ Full support |
| Linux | Debian 11+, Ubuntu 20.04+, Fedora 38+, Arch | ✅ Full support |
| macOS | Apple Silicon (M1+) and Intel (x86-64) | ✅ Universal binary |
| Android | 8.0+ (API 26+) | ✅ Companion only |
Linux Dependencies
On a fresh Linux install, you may need these system libraries:
# Debian / Ubuntu
sudo apt install libgtk-3-0 libblkid1 liblzma5 libgcrypt20
# Fedora
sudo dnf install gtk3 libblkid xz-libs libgcrypt
# Arch
sudo pacman -S gtk3 util-linux xz libgcrypt
These are typically already present on desktop Linux. The AppImage bundles most dependencies.
GPU Support
| GPU Vendor | API | Supported | Memory Required |
|---|---|---|---|
| NVIDIA | CUDA | ✅ Yes | 4 GB+ VRAM recommended |
| AMD | ROCm / Vulkan | ✅ Yes (via llama.cpp) | 4 GB+ VRAM recommended |
| Apple | Metal (MPS) | ✅ Yes | Unified memory |
| Intel | SYCL / Vulkan | ⚠️ Experimental | Varies |
For NVIDIA GPUs, install CUDA Toolkit 11.7+ and NVIDIA drivers 525+. For AMD, ROCm 5.7+ is recommended. Apple Silicon works out of the box via Metal.
Model Size vs. Hardware Guide
| Model Size | RAM (CPU only) | RAM + VRAM (GPU offload) | Typical Speed |
|---|---|---|---|
| 1–3B (Gemma 3 4B, Phi-3 mini) | 4–6 GB | 4 GB RAM + 2 GB VRAM | Fast on CPU, instant on GPU |
| 7–8B (Llama 3.1 8B, Qwen 3 8B) | 8–12 GB | 8 GB RAM + 4 GB VRAM | Usable on CPU, fast on GPU |
| 12–14B (Qwen 2.5 14B) | 12–16 GB | 12 GB RAM + 6 GB VRAM | Slow on CPU, fast on GPU |
| 32B+ | 24+ GB | 16 GB RAM + 12 GB VRAM | Requires GPU |
The Model Store shows hardware-fit badges for each download — "Fits your device", "CPU OK", or "Too large" — computed from your detected RAM and VRAM.
Embedding Model Requirements
The RAG memory system uses a separate embedding model (e.g., nomic-embed-text-v1.5). This is a ~137M parameter model that runs on CPU efficiently regardless of GPU presence. It requires ~500 MB RAM and processes ~100 tokens/second on a modern CPU.
Embedding models are lightweight by design. You can run cloud AI for chat (OpenAI, Claude) while a local embedding model handles memory independently — giving you the best of both worlds.
Storage Over Time
The data directory grows with usage:
| Component | Initial | After 1 Month (est.) |
|---|---|---|
| Models | 2–16 GB | Depends on downloads |
| Memory (RAG store) | < 1 MB | ~50–200 MB |
| Sessions (chat history) | < 1 MB | ~10–100 MB |
| WhatsApp messages | 0 | ~50–500 MB |
| Calendar events | < 10 KB | < 1 MB |
| Cloud sync cache | 0 | ~10 MB |
Memory consolidation runs daily, merging near-duplicate embeddings (cosine similarity ≥ 0.92) to keep the RAG store compact.