Row 6536

Row ID: 6536 | Dataset Entry | Axioma AXP Content Repository

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For most use cases, your mileage will be really poor for 1k. Kaggle and Google colab offer better value for money at the price point that interests you. They are free and get the job done at the beginner level.

Afaik, all modern Nvidia cards support CUDA. Yes pytorch works with dual (read: multiple) gpus. You can look at dataparallel or distributeddataparallel. I don't know about the r5 CPU, but you would need enough pcie lanes on the cpu and the motherboard to accomplish this. You can look at timm detmers blog for more details. ([link](https://timdettmers.com/))

What are you trying to accomplish in the NLP space? Do inference? Do training?

Depending on the size of models you want to run, you need to realistically look at a 5-10k usd budget for the entire rig (depending on your local market conditions), for anything involving training language models. You _can_ work with small models, but since you've asked your question in 2024, I assume it's some modern LM. you can potentially finetune smaller models like Bert or distilbert, or even a frozen CLIP with an adapter layer under 10gb, but anything beyond that will be a challenge (read: functionally impossible).

If you want to use llama cpp, just add more sticks of ram, and I think you'll be fine.

FieldValue
text For most use cases, your mileage will be really poor for 1k. Kaggle and Google colab offer better value for money at the price point that interests you. They are free and get the job done at the beginner level. Afaik, all modern Nvidia cards support CUDA. Yes pytorch works with dual (read: multiple) gpus. You can look at dataparallel or distributeddataparallel. I don't know about the r5 CPU, but you would need enough pcie lanes on the cpu and the motherboard to accomplish this. You can look at …
label r/pytorch
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communityName r/pytorch
datetime 2024-05-11
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Raw Record

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  "text": "For most use cases, your mileage will be really poor for 1k. Kaggle and Google colab offer better value for money at the price point that interests you. They are free and get the job done at the beginner level.\n\nAfaik, all modern Nvidia cards support CUDA. Yes pytorch works with dual (read: multiple) gpus. You can look at dataparallel or distributeddataparallel. I don't know about the r5 CPU, but you would need enough pcie lanes on the cpu and the motherboard to accomplish this. You can look at timm detmers blog for more details. ([link](https://timdettmers.com/))\n\nWhat are you trying to accomplish in the NLP space? Do inference? Do training? \n\nDepending on the size of models you want to run, you need to realistically look at a 5-10k usd budget for the entire rig (depending on your local market conditions), for anything involving training language models. You _can_ work with small models, but since you've asked your question in 2024, I assume it's some modern LM. you can potentially finetune smaller models like Bert or distilbert, or even a frozen CLIP with an adapter layer under 10gb, but anything beyond that will be a challenge (read: functionally impossible).\n\nIf you want to use llama cpp, just add more sticks of ram, and I think you'll be fine.",
  "label": "r/pytorch",
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