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by zozbot234
41 days ago
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Keep in mind, I said serving many requests in parallel, not just many users. In fact it's even more efficient if you can batch the requests of a large subagent swarm in parallel since this allows for sharing a big chunk of context/KV cache not just the model weights. That's why I raised the possibility of leveraging this same efficiency with DeepSeek V4. If as a user I can get into the habit of just firing off a request to be cranked on in the background and be completed whenever, and I reach a compute-limited performance workload (just like the big inference labs that serve many users concurrently, only on a smaller scale since the overall compute bottleneck hits sooner) that's quite new wrt. local models. It used to be that we could only do that by spending huge amounts of money on very fast RAM and/or scaling out to multiple nodes. A big cloud vendor does not face the same opportunity, they cannot leverage the repurposing of your own existing hardware. And they'll definitely want to minimize latency in order to get maximum throughput/utilization from the hardware they did buy, even at an emergy cost. That's why I was careful to note latency as a possible factor before. |
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