Row 53478

Row ID: 53478 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 53478 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Has anyone noticed that Microsoft is now including 40 TOPS NPUs in all their new PCs, specifically the Copilot\_PC series?

I found this fascinating and decided to ask my AI about the computational equivalence to large models like GPT-4 and GPT-3.

Here’s what I learned:

* `A 40 TOPS NPU translates to 0.04 petaFLOPs.` * `To match the GPT-4's estimated power (around 200 petaFLOPs), you'd need approximately 5,000 such NPUs.` * `For GPT-3, which used about 364 petaFLOPs during its training phase, about 9,100 NPUs would be required.`

These estimates consider the enormous computational requirements during the training phase, which far exceed those for standard operational tasks such as responding to inquiries.

Given this, could we potentially see a future where NPUs are available for hire, similar to SETI@home, where consumers could rent out their processing power for additional credits in Copilot?

This could be a viable solution to the high energy demands discussed by Sam, shifting the cost to users. In today's tech landscape, it might not be too far-fetched.

FieldValue
text Has anyone noticed that Microsoft is now including 40 TOPS NPUs in all their new PCs, specifically the Copilot\_PC series? I found this fascinating and decided to ask my AI about the computational equivalence to large models like GPT-4 and GPT-3. Here’s what I learned: * `A 40 TOPS NPU translates to 0.04 petaFLOPs.` * `To match the GPT-4's estimated power (around 200 petaFLOPs), you'd need approximately 5,000 such NPUs.` * `For GPT-3, which used about 364 petaFLOPs during its training phase, …
label r/chatgpt
dataType post
communityName r/ChatGPT
datetime 2024-05-22
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Raw Record

{
  "text": "Has anyone noticed that Microsoft is now including 40 TOPS NPUs in all their new PCs, specifically the Copilot\\_PC series?\n\nI found this fascinating and decided to ask my AI about the computational equivalence to large models like GPT-4 and GPT-3.\n\nHere’s what I learned:\n\n* `A 40 TOPS NPU translates to 0.04 petaFLOPs.`\n* `To match the GPT-4's estimated power (around 200 petaFLOPs), you'd need approximately 5,000 such NPUs.`\n* `For GPT-3, which used about 364 petaFLOPs during its training phase, about 9,100 NPUs would be required.`\n\nThese estimates consider the enormous computational requirements during the training phase, which far exceed those for standard operational tasks such as responding to inquiries.\n\nGiven this, could we potentially see a future where NPUs are available for hire, similar to SETI@home, where consumers could rent out their processing power for additional credits in Copilot?\n\nThis could be a viable solution to the high energy demands discussed by Sam, shifting the cost to users. In today's tech landscape, it might not be too far-fetched.",
  "label": "r/chatgpt",
  "dataType": "post",
  "communityName": "r/ChatGPT",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1Vb0ZjWlhxRkkwX2JzNHJRMlE4dmJFcDcyT0ZxVWM5eXcydGVva25sUEtyczlhVjVCYVZUQXZnLTNtOHBfVlJLTEpBN2VQYnNUYVZUV2dhUnZTa0UxSGc9PQ==",
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