GPUHub

On-demand cloud GPUs for AI/ML.
Launch GPU servers in seconds.
Flexible, scalable, pay-as-you-go.

[GPUHub @hub_gpu]

WisePaper × GPUhub

Building the Vibe Research ecosystem.

Structure your ideas. @wispaper
Run your models. @hub_gpu
Ship faster.

#VibeResearch
#AIResearch
#MachineLearning
#LLM

[Shaddowles | AI & GPU @Shaddowles]

Full breakdown Its here.. https://t.co/VjGTtNlHah
You can ready fully..

I hope this helps..

[Shaddowles | AI & GPU @Shaddowles]

📊 32 Research Tools to Level Up Your Paper (2026)

From literature review to publication — every tool you need for efficient, productive research.

✅ Free tools
✅ Paid tools (worth it)
✅ All pricing verified

#Research #AcademicTwitter #DataScience #PhDLife #ResearchTools #AI #Productivity

[Amy Da Fucq @SprangersAmy]

I wasted ~$100+ testing GPU clouds so you don't have to

47 hours, 5 providers, 1 Llama-3-70B fine-tuning

winner: GPUhub at $16.92
loser: Lambda at ~$45 (storage fees got me)

wrote it all up here:
https://t.co/e2D1WC4FCP

hope it helps 🙏

[GPUHub @hub_gpu]

https://t.co/SZo2sYh7XE

Real pricing. Real testing. No sponsorships. This is one of the few GPU cloud comparisons that actually feels unbiased. If you’re choosing between providers, this is worth a read.

[WisPaper @wispaper]

Our Scholar Agent is officially here to transform your research workflow! 🎉🎉🎉

From initial brainstorming to technical execution, WisPaper’s Scholar Agent handles the heavy lifting:

1⃣Idea Discovery
Say goodbye to idea drought. Through Socratic dialogue, our AI helps you clarify research directions, define problem motivation, and develop a complete research plan.

2⃣Literature Review
Automatically process up to 200 papers at once. Generate in-depth academic reviews (200,000+ words) overnight with customizable length and scope.

3⃣Paper Reproduction
Fully automated from information gathering to code execution. Includes one-click access to GPU servers with multiple configuration options for seamless deployment.

4⃣Academic Background Analysis
Instantly assess a paper’s value and map out key academic landscapes, including identifying leading research groups in fields like NLP.

Ready to supercharge your scholarship? Try the new Scholar Agent now!

[https://t.co/3wB6T6T8lq]

#WisPaper #ScholarAgent #AcademicTwitter #AIforResearch #ResearchInnovation #ResearchTools #PhDLife

[Shaddowles | AI & GPU @Shaddowles]

@hub_gpu This is very interesting..

[Shaddowles | AI & GPU @Shaddowles]

@hub_gpu This is real..😁😁

[Shaddowles | AI & GPU @Shaddowles]

🧵 @hub_gpu Benchmark: RTX 5090 vs 4090 vs 4080S

Tested 48h for LLM fine-tuning. Here's what you need to know:

[Abdur Rahim @_ARahim_]

The full Gemma 4 family is now fine-tunable on your Mac! 🍏✨

You can now fine-tune the entire family locally with mlx-tune:
✅ E2B & E4B (Text, Vision, Audio)
✅ 26B-A4B MoE & 31B Dense (Text, Vision)

The best part? No complex routing. One unified FastVisionModel API handles it all. Want to train the built-in Conformer for ASR or Audio QA? Just flip the boolean flags and hit train.

5 complete example scripts are up in the repo! 👇
https://t.co/hZduXosA8b

@GoogleDeepMind @googledevs @GoogleResearch @GoogleAI @awnihannun

[Shaddowles | AI & GPU @Shaddowles]

Most “AI case studies” I see feel like marketing slides.

The ones I actually care about are the simple, honest ones:
– here’s what we tried
– here’s how long it took
– here’s how much VRAM and money it actually used

@hub_gpu is starting to collect stories in that direction — more “real experiments”, less buzzwords. If you’re into that kind of thing, worth keeping an eye on 👇
https://t.co/3xA6QQHnng

#MachineLearning #CloudGPU #MLOps

[Salman Khan @SalmanKhan72852]

I trained a large-scale image generation model using 8× RTX 5090 GPUs on GPUHub (@hub_gpu ) — and it was surprisingly smooth.
Here’s what happened 👇
1/3

#AI #GenerativeAI #StableDiffusion #ComfyUI #CloudGPU #MachineLearning #DeepLearning #AIArt #GPU #Tech #MLOps #BigData

[Shaddowles | AI & GPU @Shaddowles]

This is what my “ML lab” looks like now:
– modest machine at home
– rent a 24GB GPU only when I actually need it
– run YOLO/SDXL/LLM experiments end‑to‑end, then shut it down

Instead of a 4090 in my room, I get a GPU I can turn on/off like this 👇

[Shaddowles | AI & GPU @Shaddowles]

Different GPU options I’ve used:
– Colab/Kaggle → great for demos, sessions/timeouts get in the way for multi‑hour training
– RunPod/Vast → lots of raw power, but node quality/configs vary, you need to babysit jobs
– local GPU → nice latency, but you pay in upfront cost + maintenance

For most of my workloads (YOLO on non‑toy data, SDXL, 7B LoRA), the best trade‑off so far has been:
– rent a 24–32GB GPU
– treat it like a lab bench (spin up → experiment → shut down)

I’ve been using GPUhub @hub_gpu for that pattern:
https://t.co/mppKZkcSar

[Salman Khan @SalmanKhan72852]

Deployed on GPUHub @hub_gpu
(Singapore-A | G067-R | RTX PRO 6000 — ~$0.91/hr)
Setup was simple, & within minutes I had a powerful environment ready.

Built a full ComfyUI pipeline with:
✔️ Stable Diffusion
✔️ ControlNet (structured outputs)
✔️ InstantID (consistent faces)
2/3

[Salman Khan @SalmanKhan72852]

I started building generative AI workflows locally… but quickly hit GPU limits 😅
Slow inference, memory issues, constant crashes.
So I moved everything to the cloud.
1/2
#AI #GenerativeAI #StableDiffusion #ComfyUI #CloudGPU #MachineLearning #DeepLearning #AIArt #GPU #Tech

[GPUHub @hub_gpu]

Can AI understand code from screenshots? Yes. We tried it with Qwen2-VL-2B.
https://t.co/YHnN5omCqw

[GPUHub @hub_gpu]

Trained YOLOv8 on VisDrone with an RTX 5090. No instability. No wasted time. Dedicated GPUs hit different. #AI #MachineLearning #YOLOv8 #GPU #DeepLearning

https://t.co/sk5AMXYo01

[Abdur Rahim @_ARahim_]

Yaay! 🎉 Just noticed the repo crossed 800+ stars and 50+ forks.

It is honestly the best feeling to see a personal project you built for your own workflow actually helping out so many other developers.

Huge thanks to everyone testing it and contributing!

[Salman Khan @SalmanKhan72852]

Trained YOLOv8 on the VisDrone dataset (dense aerial scenes) & ran the full workflow end-to-end training, validation, inference, & export.
100 epochs finished in ~1.1h with solid results (mAP50 ≈ 0.41).
Handled crowded scenes surprisingly well.
Great experience, TY @hub_gpu 👏