I’ve been doing a deep dive into the hardware requirements for local AI development this year, and the landscape has completely shifted. We are officially past the era of just “chatting” with single models. Multi-agent orchestration (using frameworks like LangGraph and CrewAI) is the new standard. To put it in perspective: recent benchmarks show single-agent setups struggling with a 2.92% success rate on complex reasoning, while multi-agent orchestration hits 42.68% . But there is a massive catch: The KV Cache Bottleneck. Running multiple agents concurrently say, a 70B “Manager” and two 14B “Workers”—requires an insane amount of memory. A 70B model with 4-bit quantization (INT4) needs about 45GB of VRAM just for the weights. Add a 128K context window, and you need another ~40GB just for the KV Cache alone. If your model spills over from VRAM into system RAM, your tokens-per-second drop to zero. The takeaway: CPU clock speeds and NPU “TOPS” marketing stickers don’t matter for developers. Choose your hardware based entirely on the context windows and VRAM your logic demands. submitted by /u/Remarkable-Dark2840
Originally posted by u/Remarkable-Dark2840 on r/ArtificialInteligence