Brendan McKeag

Discover how NVIDIA A40 GPUs on Runpod offer unmatched value for machine learning—high performance, low cost, and excellent availability for fine-tuning LLMs.

The Runpod dashboard just got a streamlined upgrade. Here's a quick look at what’s moved, what’s merged, and how new UI changes will make managing your pods and templates easier.

Learn how to extend the context length of LLaMa-2 models beyond their defaults using alpha_value and NTK-aware RoPE scaling—all without sacrificing coherency.

Join Runpod CEO Zhen Lu and Data Science Dojo CEO Raja Iqbal on October 11 for a live fireside chat about GPU-powered AI transformation and the future of scalable machine learning infrastructure.

Runpod partners with RandomSeed to power easy-to-use API access for Stable Diffusion through AUTOMATIC1111, making generative art more accessible to developers.

Runpod has partnered with Data Science Dojo to power their Large Language Model bootcamps, providing scalable GPU infrastructure to support hands-on learning in generative AI, embeddings, orchestration frameworks, and deployment.

Falcon-180B is the largest open-source LLM to date, requiring 400GB of VRAM to run unquantized. This post explores how to deploy it on Runpod with A100s, L40s, and quantized alternatives like GGUF for more accessible use.