| Variant Name | VRAM Requirement | Recommended Configuration | Best Use Case |
|---|---|---|---|
| 70b | 43GB | Mac Studio (M2 Ultra 128GB) | General-purpose inference |
| 70b-instruct-fp16 | 141GB | Mac Studio Cluster (2x M2 Ultra 192GB) | High-precision fine-tuning and training |
| 70b-instruct-q2_K | 26GB | Mac Studio (M1/M2 Ultra 64GB) | Lightweight inference with reduced precision |
| 70b-instruct-q3KM | 34GB | Mac Studio (M1/M2 Ultra 64GB) | Balanced performance and efficiency |
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| Perfect! I found the aggregated results. Let me parse this data: | |
| The test results show: | |
| - 0% error rate across all endpoints! (Failure Count = 0 for all) | |
| - Total requests: Let me add them up: | |
| - About Page: 1626 | |
| - Bill Discussion Tab: 1028 | |
| - Bill Summary: 1042 | |
| - Homepage: 2533 | |
| - Login Page: 847 |
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| https://www.cnbc.com/2025/01/24/target-rolls-back-major-dei-initiatives.html?__source=sharebar|twitter&par=sharebar | |
| Target | |
| Walmart | |
| TractorSupply | |
| Meta | |
| McDonalds | |
| https://fortune.com/2025/01/23/dei-donald-trump-executive-order-presidential-action-what-it-means-for-businesses/ | |
| Ford | |
| Lowes |
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| def largestSum(ns: List[Int]): Int ={ | |
| def sum(numbers: List[Int]) = numbers.foldLeft(0)((a,b) => a+ b) | |
| @tailrec def loop(numbers: List[Int], large: Int): Int = numbers match{ | |
| case Nil => large | |
| case _ => { | |
| val thisSum = sum(numbers) | |
| if(thisSum > large) | |
| loop(numbers.tail, thisSum) | |
| else |