How to Fine-Tune LLMs with Axolotl on Runpod
9d
- Published
- Collected
Learn how to fine-tune large language models using Axolotl on Runpod. This guide covers LoRA, 8-bit quantization, DeepSpeed, and GPU infrastructure setup.
overfeed.news indexes and links. We publish a short excerpt — the full article stays at RunPod Blog.
More from RunPod Blog
Log in to follow this source
Serverless GPU inference vs. GPU Pods: cost benchmarks
We benchmarked one A40 as a Pod and as a Serverless worker. A busy worker cost about 2.3x per token, but Serverless wins below 29 billed minutes an hour.
Batch Inference Is Now Generally Available on Runpod
Batch Inference is now generally available on Runpod Serverless. Send up to 3 million requests a day to an existing queue-based endpoint, on dedicated workers.
Runpod Q3 2026: Batch Inference, Global Volumes, Enterprise
See what shipped on Runpod in Q3 2026, from Batch Inference and Global Volumes to Cluster Expansion, REST API v2 and Organizations for Runpod Enterprise..
Benchmarking LLMs: A Deep Dive into Local Deployment & Optimization
Curious how local LLM deployment stacks up? This post explores benchmarking strategies, optimization tips, and what DevOps teams need to know about.