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@laura_garcia shared a post, 1 day, 21 hours ago

Software Developer, RELIANOID

Network Load Balancer

🌐 𝘎𝘳𝘦𝘢𝘵 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘴𝘵𝘢𝘳𝘵𝘴 𝘸𝘪𝘵𝘩 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘵𝘳𝘢𝘧𝘧𝘪𝘤 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯. A 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 helps organizations maximize availability, eliminate bottlenecks, and keep critical applications running smoothly—even under heavy traffic or unexpected failures. In our latest article, we explain how Layer 4 l..

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@laura_garcia shared a post, 2 days, 18 hours ago

Software Developer, RELIANOID

SOC2 compliance

🔐 𝗦𝗢𝗖 𝟮 alignment is about trust, resilience, and doing security right by design. At 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗, our load balancing and application delivery platform is aligned with the 𝗦𝗢𝗖 𝟮 𝗧𝗿𝘂𝘀𝘁 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮—𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹𝗶𝘁𝘆, 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗣𝗿𝗶𝘃𝗮𝗰𝘆. From encryption ..

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@kevin-faun started using tool BOOM , 2 days, 20 hours ago.

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@goutham-annem started using tool vLLM , 3 days, 2 hours ago.

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@goutham-annem started using tool Kubernetes , 3 days, 2 hours ago.

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@goutham-annem started using tool Istio , 3 days, 2 hours ago.

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@goutham-annem started using tool GPT-5.3-Codex , 3 days, 2 hours ago.

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@goutham-annem started using tool Google Kubernetes Engine (GKE) , 3 days, 2 hours ago.

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@goutham-annem started using tool Claude Code , 3 days, 2 hours ago.

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@goutham-annem started using tool Azure Kubernetes Service (AKS) , 3 days, 2 hours ago.

Unsloth is an open-source toolkit for training and fine-tuning large language models faster and with less memory than a standard Hugging Face stack. Its core library replaces PyTorch's default autograd with custom backpropagation kernels written in OpenAI's Triton language, which is where most of its speed and memory savings come from. It supports LoRA, QLoRA, full fine-tuning, reinforcement learning, pretraining, and 4-bit, 16-bit, and FP8 training, across more than 500 text, vision, audio, and embedding models.

The practical draw is hardware reach. QLoRA workflows in Unsloth let you fine-tune an 8B model on a single 12 GB consumer GPU, and the project headlines roughly 2x faster training with about 70 percent less VRAM versus baseline implementations, though the exact figures vary by model, GPU, and config. A 2026 update added faster mixture-of-experts training, with models like Qwen3-30B-A3B fine-tunable on about 17.5 GB of VRAM. It runs on NVIDIA (including Blackwell and DGX Spark), AMD, and Intel GPUs, with free Colab and Kaggle notebooks for trying it without local hardware.

It fits cleanly into the local-AI workflow. Unsloth integrates with Hugging Face transformers and TRL, and uses llama.cpp to save and run models, exporting to GGUF for Ollama or LM Studio as well as safetensors. As of 2026 it also ships Unsloth Studio, a local no-code GUI that covers the full lifecycle from dataset creation to training to running and comparing GGUF and safetensors models, with tool-calling, web search, and an OpenAI-compatible API, all running offline on Mac and Windows, with the core library under the Apache 2.0 license.