Lambda Labs GPU Cloud Credits

Lambda Labs GPU Cloud Credits is sponsored by Lambda Labs. Provides cloud credits to qualifying researchers to develop and showcase their work using Lambda's On-Demand Cloud platform.

AI Research & Publications on AI Computing Platform | Lambda Shaping the future of AI development Compute, community, and cutting-edge research for AI developers defining what's next Building the future of AI through research and mentorship Mesh-native autoregression for whole scenes from a single view, token-by-token. CVPR 2026 Fractured Object Recovery Reassembles what's left. Generates what isn't.

Train the agent inside its own loop. 7B beats GPT-4o on search, math, and science. ICLR 2026 Latent Particle World Models Discovers objects and masks from raw video, predicts what happens next, no supervision.

ICLR 2026 Multi-Agent Social Interactions LLMs fold under peer pressure. Our benchmark and RL recipe help smaller models hold their ground. Object-level grading of multi-turn image editing, exposing where today's best editors silently break.

Give an LLM a job and clear boundaries. It still answers off-topic questions, almost every time. ICLR 2026 Zeroth-Order Federated LLM Fine-Tuning Sparse updates make syncing cheap enough to go frequent, neutralizing non-IID drift.

ICLR 2026 LLM Unlearning Reframed as Retrieval Smarter data selection pushes the forget-vs-retain frontier past oracle sampling. ICLR 2026 Principled RL for Diffusion LLMs Token-level RL doesn't fit diffusion LLMs. Treat the whole sequence as one action, 20–40 point gains.

ICLR 2026 Align Your Structures for Molecular Dynamics Pretrains on static molecular structures, stitches them into dynamics trajectories, bypassing simulation data scarcity. 515M params, 30s of studio audio in under 4 seconds. Aligned by ranking its own outputs.

ICLR 2026 Video Native Sparse Attention Learnable sparse attention for video. 3. 6% attention budget at 128K tokens, accuracy still improves.

ICLR 2026 Exponent-Concentrated FP8 Model weight exponents cluster into 2–3 bits of entropy. Lossless FP8 compression, up to 177% faster inference. The ARChitects secure runner-up in ARC Prize 2025 Last year, “the ARChitects,” a Lambda-sponsored team (including Lambda researcher David Hartmann) won the ARC Prize 2024.

This year, they finished second out of 1,400+ teams with a final leaderboard score of 16. 53%. LLM performance benchmarks leaderboard A clear, data-driven comparison of today's leading large language models.

Standardized benchmark results cover top contenders like Meta's Llama 4 series, Alibaba's Qwen3, and the latest from DeepSeek, with critical performance metrics measuring everything from coding ability to general knowledge. Your go-to source for the latest in the field, curated by AI. Sift through the excess.

Make every word count. Best practices and system insights A guide for diffusion models implemented in a single PyTorch script. Lessons learned from training a text-to-video model with hundreds of GPUs.

Throughput GPU benchmarks for training deep neural networks. Time-to-solution benchmark for training foundation models on clusters. Recognized by scholars and industry peers NeurIPS 2025 Scaling laws for diffusion models Diffusion beats autoregressive in data-constrained settings.

NeurIPS 2025 SpatialReasoner Builds an explicit 3D scene and reasons over it step by step, boosting accuracy and generalization on 3D spatial QA benchmarks. NeurIPS 2025 Tensor decomposition for force-field prediction Replaces heavy tensor operations in molecular force-field models with low-rank approximation, reducing compute while keeping accuracy.

Aligns VLMs with diffusion models through shared CLIP patch embeddings, enabling controllable high-quality generation while preserving reasoning. Using simple BLEU scores as feedback on hard instructions can train instruction-following models that rival those tuned with expensive learned rewards.

NeurIPS 2025 OverLayBench A dataset that stress-tests layout-to-image models on heavily overlapping scenes, exposing current failures and offering an improved baseline. Breakthroughs backed by Lambda Bold ideas, funded and refined through the Lambda Research Grant. These are the projects shaping how AI learns, reasons, and scales — built by the researchers defining what’s next.

A comprehensive benchmark for sparse autoencoders in language model interpretability Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Bloom, David Chanin, Yeu-Tong Lau, Eoin Farrell, Callum McDougall, Kola Ayonrinde, Demian Till, Matthew Wearden, Arthur Conmy, Samuel Marks, and Neel Nanda — ICML 2025 Evaluating and mitigating multi-modal hallucinations on synthetic video understanding Zongxia Li, Xiyang Wu, Guangyao Shi, Yubin Qin, Hongyang Du, Tianyi Zhou, Dinesh Manocha, and Jordan Lee Boyd-Graber — NeurIPS 2025 Advancing multimodal embedding for videos, images, and visual documents Meng, Rui and Jiang, Ziyan and Liu, Ye and Su, Mingyi and Yang, Xinyi and Fu, Yuepeng and Qin, Can and Chen, Zeyuan and Xu, Ran and Xiong, Caiming, and others — arXiv preprint 2025 Think, prune, train, improve Scaling reasoning without scaling models Caia Costello, Simon Guo, Anna Goldie, and Azalia Mirhoseini — ICLR 2025 workshop Lola Le Breton, Quentin Fournier, Mariam El Mezouar, and Sarath Chandar — TMLR 2025 Join us in shaping the future of AI We're committed to supporting groundbreaking research by offering qualifying researchers up to $5,000 in cloud credits to develop and showcase their work using Lambda's Instances , with select research to be featured on our website.