free Kaggle dual T4s GPUs Running Meta’s Muse Glimmer 30B Q6_K_XL. - 130,000 token context - 26.5 GB VRAM - Prefill: 265 t/s - Decode: 9 t/s - Cost: $0 Aggressive 16:1 GQA. Unquantized KV cache for 130k tokens is only 2 GB.

While the Kaggle competition demonstrated AIRA₃’s capabilities in a specific domain, the system itself can generalize across distinct domains: changing only the task specification. In an internal benchmark, AIRA₃ achieved a 27% latency reduction on production GPU kernels, and gold-level performance in another Kaggle competition translating 4,000-year-old Akkadian clay tablets into English. We're early, and hard problems are still ahead of us. But we believe a system that compounds its own knowledge is the right bet. As we continue to develop and scale, we’re excited about its potential to accelerate AI research and unlock recursive self-improvement.

Over 5,000 Kagglers. One @nvidia challenge. Five key takeaways. 👇 Learn how developers fine-tuned reasoning models using Google Cloud G4 VMs. With compute taken care of, teams focused entirely on improving accuracy:

Visit the Kaggle Game Arena today at 9:30am at kiosk #2# where frontier models compete in live environments. Stop by to browse benchmarks and leaderboards with Yuting Han, Lucy He, and Tiffany Xiang at the Google booth (#B206#). #ICML2026#

ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwriting, and complex tables. The benchmark covers 370 enterprise documents across 8 business domains and 67 document types. See how LlamaParse, Codex, Claude Code, and more stack up. Read the full story →

Free GPUs 2x NVIDIA T4s & run Qwen 3.8 27B at 14 t/s with 120k context - 120k context on FREE Kaggle T4s. - FP16 KV - 14 t/s - No credit card. - No expensive GPU. - Total VRAM used: only 26.6 GB across both cards. - Under 5 minutes setup. - Kaggle gives you 30 hours/week of dual T4 for free. Free kaggle notebook from Alok : -

@karinanguyen There's also MLE-Bench, which is ecologically valid (tasks come from real kaggle competitions) and involves building a very diverse set of ML apps to solve specific problems. The same progress shows up here.

The Vesuvius Challenge is still a CHALLENGE! 🧠📜 We’re teaming up with @kaggle to launch a $100K Surface Detection competition on 3D CT scans of the Herculaneum scrolls: . Train topology-aware models, climb the leaderboard, and help us read an ancient library! #VesuviusChallenge# #kaggle# #computervision# #machinelearning#

As a test of our progress to advance the frontier of AI research, in June we entered the next generation of our autonomous AI research system, AIRA₃, in a live Kaggle competition run by NVIDIA to fine-tune a 30B Nemotron model. The challenge was to teach the model to reason better — all competitors had access to the same information and were graded externally on a private test set. AIRA₃ placed 8th out of ~4,000 teams to win Gold, outperforming human competitors who had access to the same frontier tools. We believe this is a reliable signal that AIRA₃ can improve a targeted capability of an AI model at a level similar to human experts.