GPU Selection for Local AI Agent Workloads
GPU Selection for Local AI Agent Workloads
Running AI agents locally means running inference locally. Your GPU choice determines how fast your agent thinks, how large a model it can load, and whether the whole setup is practical for daily use.
Apple Silicon - The Unified Memory Advantage
For macOS AI agents, Apple Silicon is the default choice and often the best one. The M-series chips share memory between CPU and GPU, which means a Mac with 64GB or 128GB of unified memory can load models that would require an expensive dedicated GPU on other platforms.
- M4 Pro/Max - Handles 7B-13B parameter models comfortably with fast inference
- M2/M4 Ultra - Can run 70B models with enough memory, though slowly
- Key advantage - No VRAM limitation separate from system RAM
For agents like Fazm that run on macOS, Apple Silicon is the natural fit. You get decent inference speed without a separate GPU purchase.
NVIDIA - The Raw Performance Play
If you are running a Linux homelab or want maximum inference speed:
- RTX 4090 (24GB VRAM) - Fast inference for models up to 13B at full precision, larger models with quantization
- RTX 5090 (32GB VRAM) - More headroom for 30B+ models
- Dual GPU setups - Can split larger models across cards, but adds complexity
NVIDIA's CUDA ecosystem has the best software support. Most inference engines are optimized for it first.
AMD - Improving but Behind
AMD GPUs offer better price-per-VRAM but lag in software support:
- ROCm support is improving but still less reliable than CUDA
- RX 7900 XTX (24GB) - Competitive hardware, but driver and framework issues persist
- Best suited for users comfortable debugging compatibility problems
What Actually Matters for Agent Workloads
AI agents do not just run inference - they also process screenshots, parse accessibility trees, and manage tool calls. The model inference is often the bottleneck, but not always. For most desktop agent use cases:
- Memory capacity matters more than raw compute speed
- Quantized models (Q4, Q5) give 90% of full precision quality at half the memory
- Inference latency under 500ms is the threshold where agent interactions feel responsive
Start with what you have. Upgrade only when you hit a specific bottleneck.
Fazm is an open source macOS AI agent. Open source on GitHub.