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Hacker News
@h4ckernews@mastodon.social  ·  activity timestamp 2 weeks ago

Don't Download Apps

https://blog.calebjay.com/posts/dont-download-apps/

#HackerNews #AppSafety #DigitalWellbeing #TechAdvice #OnlineSecurity

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Debby ‬⁂📎🐧:disability_flag:
@debby@hear-me.social  ·  activity timestamp 3 months ago

Hey everyone 👋

I’m diving deeper into running AI models locally—because, let’s be real, the cloud is just someone else’s computer, and I’d rather have full control over my setup. Renting server space is cheap and easy, but it doesn’t give me the hands-on freedom I’m craving.

So, I’m thinking about building my own AI server/workstation! I’ve been eyeing some used ThinkStations (like the P620) or even a server rack, depending on cost and value. But I’d love your advice!

My Goal:
Run larger LLMs locally on a budget-friendly but powerful setup. Since I don’t need gaming features (ray tracing, DLSS, etc.), I’m leaning toward used server GPUs that offer great performance for AI workloads.

Questions for the Community:
1. Does anyone have experience with these GPUs? Which one would you recommend for running larger LLMs locally?
2. Are there other budget-friendly server GPUs I might have missed that are great for AI workloads?
3. Any tips for building a cost-effective AI workstation? (Cooling, power supply, compatibility, etc.)
4. What’s your go-to setup for local AI inference? I’d love to hear about your experiences!

I’m all about balancing cost and performance, so any insights or recommendations are hugely appreciated.

Thanks in advance! 🙌

@selfhosted@a.gup.pe #AIServer #LocalAI #BudgetBuild #LLM #GPUAdvice #Homelab #AIHardware #DIYAI #ServerGPU #ThinkStation #UsedTech #AICommunity #OpenSourceAI #SelfHostedAI #TechAdvice #AIWorkstation #LocalAI #LLM #MachineLearning #AIResearch #FediverseAI #LinuxAI #AIBuild #DeepLearning #OpenSourceAI #ServerBuild #ThinkStation #BudgetAI #AIEdgeComputing #Questions #CommunityQuestions #HomeLab #HomeServer #Ailab #llmlab


What is the Best used GPU Pick for AI Researchers?
 GPUs I’m Considering:
| GPU Model            | VRAM          | Pros                                      | Cons/Notes                          |
| Nvidia Tesla M40          | 24GB GDDR5        | Reliable, less costly than V100              | Older architecture, but solid for budget builds |
| Nvidia Tesla M10          | 32GB (4x 8GB)     | High total VRAM, budget-friendly on used market | Split VRAM might limit some workloads |
| AMD Radeon Instinct MI50   | 32GB HBM2         | High bandwidth, strong FP16/FP32, ROCm support | ROCm ecosystem is improving but not as mature as CUDA |
| Nvidia Tesla V100         | 32GB HBM2         | Mature AI hardware, strong Linux/CUDA support | Pricier than M40/M10 but excellent performance |
| Nvidia A40                | 48GB GDDR6        | Huge VRAM, server-grade GPU                  | Expensive, but future-proof for larger models |
What is the Best used GPU Pick for AI Researchers? GPUs I’m Considering: | GPU Model | VRAM | Pros | Cons/Notes | | Nvidia Tesla M40 | 24GB GDDR5 | Reliable, less costly than V100 | Older architecture, but solid for budget builds | | Nvidia Tesla M10 | 32GB (4x 8GB) | High total VRAM, budget-friendly on used market | Split VRAM might limit some workloads | | AMD Radeon Instinct MI50 | 32GB HBM2 | High bandwidth, strong FP16/FP32, ROCm support | ROCm ecosystem is improving but not as mature as CUDA | | Nvidia Tesla V100 | 32GB HBM2 | Mature AI hardware, strong Linux/CUDA support | Pricier than M40/M10 but excellent performance | | Nvidia A40 | 48GB GDDR6 | Huge VRAM, server-grade GPU | Expensive, but future-proof for larger models |
What is the Best used GPU Pick for AI Researchers? GPUs I’m Considering: | GPU Model | VRAM | Pros | Cons/Notes | | Nvidia Tesla M40 | 24GB GDDR5 | Reliable, less costly than V100 | Older architecture, but solid for budget builds | | Nvidia Tesla M10 | 32GB (4x 8GB) | High total VRAM, budget-friendly on used market | Split VRAM might limit some workloads | | AMD Radeon Instinct MI50 | 32GB HBM2 | High bandwidth, strong FP16/FP32, ROCm support | ROCm ecosystem is improving but not as mature as CUDA | | Nvidia Tesla V100 | 32GB HBM2 | Mature AI hardware, strong Linux/CUDA support | Pricier than M40/M10 but excellent performance | | Nvidia A40 | 48GB GDDR6 | Huge VRAM, server-grade GPU | Expensive, but future-proof for larger models |
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Roan (they/them)
@triumphant_fool@mastodon.social  ·  activity timestamp 10 months ago

okay I'm done,
how do i migrate away from Google?
what are the best, most user friendly, non techskill requiring alternatives for
- Gmail (is proton mail good?)
- Google drive (is it worth buying a synergy nas? how easy is it to access a nas from different devices? )
- google docs/sheets (does libre office have the same ability to edit a document from multiple devices and sync the edit with the other devices?)
- google search
- Google Maps
- android
#techadvice #opensource
@rvansleen help?

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