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Runpod Blog.

DeepSeek V4 in the wild, and how to run it on Runpod New Runpod datacenter now live: AP-IN-1 Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage
Runpod RoundUp 2 – 32k Token Context LLMs and New Stabili...
Brendan McKeag · 2023-07-29 · via Runpod Blog.

Welcome to the Runpod Roundup for the week ending July 29, 2023. In this issue, we'll be discussing the newest advancements in AI models over the past week, with a focus on new offerings that you can run in a Runpod instance right this second. In this issue, we'll be looking at the new SDXL release as well as new LLM model advancements.

High Context Llama-2 Models Now Available

It's not even been a week since Meta and Microsoft released Llama-2, and the community has been hard at work...

togethercomputer has released their 32k token context version of Llama-2 7b for anyone to download off of Huggingface. Although there have been several versions of closed models (GPT-4, et al) that have had 32k token context, this is I believe the first freely available open-sourced model that has made the jump to 32k. Although many front ends such as Oobabooga do not yet support 32k context windows, that is likely to change as these models become more commonplace.

conceptofmind has also released 16k context versions of Llama 2 (called LLongMA-2) and they have 7b and 13b versions available.

(Could OpenAI also be looking into releasing an open-source LLM of their own in the not-too-distant future? Maybe...)

Stable Diffusion XL 1.0 Released For All

The long-awaited SDXL is finally out and available for use on Runpod - no research credentials required! Not to belabor the point, but SDXL really is the next generation (pun intended) of AI art, and we've got a helpful blog post that will help you get it up and running in your pod.

Two AI-generated images: a tiger in work overalls on a dock and a panda astronaut in a cafe

StabilityAI Releases Stable Beluga 1 and 2 LLMs

It's clearly been a busy week for Stability AI, as they have also released two LLMs, Stable Beluga 1 and 2. The Stable Beluga 1 entry on Huggingface only holds delta weights (likely due to licensing issues with Llama-1) but Stable Beluga 2 is the complete model available to download. The former is a Llama-1 65b model, while the latter is a Llama-2 70b version, both of which have been finetuned on Microsoft Orca style datasets. According to their press release, both models have a focus on intricate reasoning and answering complex questions relating to specialized domains requiring subject matter expertise, such as law and mathematical problem solving.

Questions?

Feel free to reach out to Runpod directly if you have any questions about these latest developments, and we'll see what we can do for you!

Author profile: Brendan McKeag