cesar (@caesar_aii) “THREE PANTHER LAKE NUCS JUST TURNED LLAMA 3.3 70B FROM A 1.4 TOKENS PER SECOND B” — TopicDigg

THREE PANTHER LAKE NUCS JUST TURNED LLAMA 3.3 70B FROM A 1.4 TOKENS PER SECOND BOTTLENECK INTO NEARLY 500 TOKENS PER SECOND OF LOCAL TEAM CAPACITY For a team sending repeatable code, document, and internal tasks, three owned lanes can matter more than one oversized model. The terminal shows the tradeoff clearly. Split Llama 3.3 70B Instruct across the NUCs and it reaches 1.4 tok/s. That makes a single shared instance a slow queue. Run a full copy on each 64 GB ASUS NUC 16 Pro instead. Send concurrent requests across the three machines over 2.5G. The reported result moves from about 196 tok/s on one node to nearly 500 tok/s under load across the cluster. Now the three small boxes are not pretending to be one server. They are separate local capacity for work that stays on the network. The boundary is still real: aggressive quantization, memory, networking, power, heat, and setup work all come with the purchase. But for steady internal queues, owned throughput can beat renting one giant model that crawls whenever everyone needs it at once. Bookmark this before your next cloud bill. Follow for local AI builds that actually change the math.