Direct Answer: In today’s market, even refurbished 16GB laptops (like ThinkPads or Latitudes) cost $180 to $280+, and basic 8GB Raspberry Pi 5 starter kits sell for $130 to $170+. Buying new nodes is inefficient. However, taking a 16GB laptop you already own and connecting it as an auxiliary node in a RAMDeck cluster contributes 12GB to 14GB of active working memory to your local pool for $0—providing the exact capacity needed to bridge the gap and run frontier 70B models smoothly on home hardware.
The Power of Modular AI Headroom
In local AI inference, capacity is binary: either the model fits into RAM, or it crashes. If your primary laptop has 32GB of RAM, you fall just short of running a 70B quantized model. Plugging an older 16GB laptop into your network pulls total addressable memory to 48GB, instantly unlocking 70B parameter inference.
Honest Network Limits: Why Gigabit Ethernet Delivers
Distributed inference relies on sequential pipeline parallelism. Because model weights stay stored in each computer’s memory, only compact activation vectors (~650 KB per token) travel between machines. On standard Gigabit Ethernet or Wi-Fi 6, this transfer takes only a few milliseconds, delivering responsive, real-time generation.
Check out the complete model-size compatibility matrix at RAMDeck.com.
👉 Back the Hardware Campaign: Reserve the RAMDeck Hardware Hub on Indiegogo for $109 →
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