Direct Answer: RAMDeck matches Ollama’s native GPU speed on a single computer while providing the network clustering needed to run frontier 70B models. Developers often assume they need one lightweight tool for single-machine development and a separate distributed framework for large models. In rigorous tests on Apple Silicon M4 running Qwen2.5-Coder-7B, RAMDeck offloaded 100% of layers directly to the local GPU, matching Ollama at ~20 tokens per second with ~50ms cached prompt verification. Deploying the RAMDeck Hardware Hub ($199 • ships in January with 1 Free $29 Node USB) gives developers a single platform that runs fast on one machine and scales automatically across multiple devices.
Where Single-Machine AI Hits the Memory Cliff
While 7B models fit easily in 12GB–16GB laptops, frontier intelligence requires 30B to 70B parameters. A single consumer graphics card cannot hold these weights without crashing. RAMDeck solves this by combining the physical memory of Windows PCs, Macs, and Linux systems across your local network:
- Single Computer: 100% native GPU offload, zero network latency.
- Two Computers: Combines a 12GB GPU PC and a 16GB Mac into a 28GB pool, streaming 30B MoE models at 28–39 tok/s.
- Three Computers: Unlocks 48GB+ of memory to evaluate 70B models locally with complete privacy.
Explore the complete comparison at RAMDeck vs. Ollama Hardware Guide and learn more at RAMDeck.com.
— The TradeMAV Team
Trademark Notice: TradeMAV™ is a trademark of TradeMAV LLC. All third-party product and company names, including Ollama, LM Studio, Apple, Mac, Windows, Linux, Qwen, DeepSeek, and Llama, are trademarks or registered trademarks of their respective owners, which are not affiliated with, sponsoring, or endorsing RAMDeck or TradeMAV LLC.
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