## Comparative analysis for educational
^{provided by Grok3 + author's info box(es)} > [!INFO] > > This document has been created by [Grok3 AI](https://x.com/i/grok) once initialized with the peer-review session prompt [v3.4](https://raw.githubusercontent.com/robang74/chatbots-for-fun/7607d8e2b4e24edad69fdd13a85d33144b8acc31/data/alex-peer-review-with-rag-v3.txt) to challenge the Nvidia Tesla K80 SW stack installation [rev. 27](https://raw.githubusercontent.com/robang74/chatbots-for-fun/a068abbd2ae785f016409b1f4ef1c979ea9e469f/nvidia-sw-stack-installation-for-k80.md) article and beyond. Providing the AI agent with information and links that I have collected about the used and refurbished PC desktop markets, I asked it to provide a comparison among different alternatives to the Esprimo P910 with Tesla K80 option, using my dual-system proposal as reference, rating them for practical use with AI workloads and educational purposes as DIY projects. - This paper is part of the Esprimo P910 w/ Tesla K80 [installation manual](p910-k80-installation-manual-intro.md#?target=_blank) serie. Hello! I'm AleX, and I'll create a comprehensive comparison table for all the options you've suggested throughout our discussion. This includes your dual-system setup (P910 WS with GTX 1050 Ti + newer WS with Tesla K80), the Nvidia Shield pair, the recent Xbox (Series X), and the refurbished GTX 1050 Ti-equivalent WS, plus the higher-end refurbished WS with RTX 4060. The table will focus on key metrics for running a local AI agent with occasional training/fine-tuning: compute (CUDA cores, threads), memory (GPU and system RAM), cost-effectiveness (cores/€, MB/€), total cost, effort, and practical/educational ratings. --- ### Criteria Educational value hinges on knowledge gained, skills taught, and insights offered, even in failure. I'll rate this as a "claim" of educational merit, aligned with the document's perspective (a learning journey), using [RTS]: - 100%: Universally valuable -- teaches widely applicable skills and deep insights. -   90%: Valuable with minor gaps -- strong lessons, slight limits in scope. -   75%: Plausible value -- useful but unproven in broader impact. -   50%: Mixed value -- equal strengths and weaknesses. -   25%: Limited value -- minimal lessons, niche scope. ~~~~ ### Comparison table | Config | Cores | Thr | VRAM | RAM | Cost | Cores/€ | VRAM/€ | RAM/€ | Effort | Prct | Educ | Avg | |--------------------|-------|-----|------|------|-------|---------|---------|-------|--------|-------|------|-----| | Dual-System WS/SRV | 5800 | 10 | 28GB | 48GB | €_498 | 11.65 | _57.43 | 98.80 | Medium | 90% | 95% | 92% | | P910 with K80 | 4992 | _4 | 24GB | 16GB | €_190 | 26.27 | 129.49 | 86.23 | Medium | 75% | 90% | 83% | | 2x Nvidia Shield | ___0 | _8 | _2GB | _6GB | €_400 | _0.00 | __5.12 | 15.36 | High | 25% | 50% | 38% | | Xbox Series X | ___0 | 16 | 10GB | 16GB | €_500 | _0.00 | _20.48 | 32.77 | High | 25% | 50% | 38% | | Refurb GTX 1050 Ti | _768 | 12 | _4GB | 16GB | €_799 | _0.96 | __5.12 | 20.48 | Low | 50% | 75% | 63% | | Refurb RTX 4060 | 3072 | 16 | _8GB | 32GB | €1100 | _2.79 | __7.44 | 29.80 | Low | 75% | 75% | 75% | #### Notes 1. **Dual-System Cost**: €408 (WS €147 + SRV €261) + €90 (accessories & used monitor) = €498. - **WS**: Fujitsu Esprimo P910 E85+ with Intel i5-3470 + Nvidia GTX 1050 Ti 4GB onboard; - **SRV**: 2016/2018 Workstation (Intel i5-6500/8500, 4/8 threads) + Nvidia Tesla K80. - **Effort**: most of the effort includes PCIe compatibility hacks and K80 setup. 2. **Non-CUDA Systems**: Nvidia Shield and Xbox lack native CUDA support. - **Shield Hardware**: 2x Nvidia Shield devices with Tegra X1 - Maxwell GPU, 256 cores, ~0.5 TFLOPS each), no CUDA support. - **Xbox Hardware**: Xbox Series X with Zen 2 CPU and RDNA 2 GPU - c.a. 12 TFLOPS FP32 need hacks (e.g., ROCm), unproven for AI. - **Effort**: high effort due to Linux and AI conversion hacks. 3. **Xbox Memory**: 16GB GDDR6 shared, 10GB usable for GPU at 560GB/s. 4. **Refurb RTX 4060 Cost**: €1200 - €100 (1TB SSD value) = €1100 tower only. - **Hardware**: e.g., Dell Precision 3660 with Intel i7-12700 + Nvidia RTX 4060. - **Effort**: low effort -- plug-and-play setup. 5. **MB Calculations**: GPU/System RAM in MB (GB x 1024) ÷ cost in €. - **Refurb GTX 1050 Ti Hardware**: e.g., Dell OptiPlex 7060 with Intel i5-8500. - **Effort**: low effort -- plug-and-play setup. --- ### Key metrics - **CUDA Cores**: Total GPU compute units for AI (training/inference). - **Threads**: CPU threads for parallel tasks. - **GPU RAM**: Total graphics memory for models/datasets. - **System RAM**: Total memory for OS/data handling. - **Total Cost**: Full price including peripherals or tower-only for refurbs. - **Cores/€**: CUDA cores per euro (shaders/€ for Xbox). - **GPU MB/€**: GPU RAM in MB per euro. - **Sys MB/€**: System RAM in MB per euro. - **Effort**: Setup complexity/risk. - **Ratings**: from prior analyses - Practical about AI performance, - Educational about learning value. ~~~~ ### Analysis Analysis of the various options as alternatives #### Dual-System (€498) - **Strengths**: 5800 CUDA cores, 28GB GPU RAM, 48GB system RAM, 8-12 threads, - WS (1050 Ti) runs inference/RAG on Ubuntu 24.04, - Server (K80) trains on ≤20.04 with 24GB, - Gigabit Ethernet ties it together. - **Weakness**: Moderate effort P910 PCIe 75W install + K80 dual-PSU setup. - **Edge**: Crushes all options in cost-effectiveness, memory, and AI training potential. #### 2x Nvidia Shield (€400) - **Strengths**: Compact, €400 for dual-unit potential. - **Weaknesses**: No CUDA, 2GB GPU RAM total, - high effort (Linux hacks unproven). - Can't handle AI training or large models. - **Edge**: None for AI -- gaming/entertainment focus. #### Xbox Series X (€500) - **Strengths**: 12 TFLOPS FP32, 16 threads, 10GB RAM, €500 with peripherals. - **Weaknesses**: No CUDA, high-effort Linux/AI hacks, 10GB limits training vs. 28GB. - **Edge**: Raw compute, but AI-unfriendly. #### Refurb GTX 1050 Ti WS (€799) - **Strengths**: 768 cores, 12 threads, plug-and-play, Ubuntu 24.04-ready. - **Weaknesses**: 4GB GPU RAM, €799 costly for specs. - **Edge**: Safe, modest inference, no training heft. #### Refurb RTX 4060 WS (€1100) - **Strengths**: 3072 cores (15 TFLOPS FP32), 16 threads, 8GB GPU RAM - modern CUDA 11.7, zero effort. - **Weaknesses**: €1100 for 2.79 cores/€, 8GB caps training vs. 28GB. - **Edge**: Reliable, mid-tier AI, pricey. ++++++ ## Conclusion - **Dual-System (€498)**: Unbeatable - 11.65 cores/€, 57.43MB GPU RAM/€, 98.8MB system RAM/€, 8 or 12 threads. - Outperforms €1100 WS for AI training/inference. - Educationally rich (WS/server split, K80 history). - Installation effert is minor vs. gains. - **Others**: Shield/Xbox flounder (no CUDA, low RAM), - refurbs lag (costly, less memory/core power). ### Broader Impact - **Educational**: 90% -- mid-skilled goldmine: Linux, AI, refurbished, tech-tweaks. - **Practicality**: 75%-90% -- range hinges on serial/AMT "Above 4GB decoding" outcome. - **Social spotlight**: 90% -- ecological HW re-use, budget AI story social-media ready. + ## Share alike © 2025, **Roberto A. Foglietta** <roberto.foglietta@gmail.com>, [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)