Learn how to speed up LLM inference by 1.4-1.6x using speculative decoding in vLLM. This guide covers Draft Models, N-Gram Matching, Suffix Decoding, MLP Speculators, and EAGLE-3 with real benchmarks on Llama-3.1-8B and Llama-3.3-70B.
Deploy NVIDIA NemoClaw with local Nemotron 3 Nano 30B inference on a JarvisLabs A100 VM. Kernel-level sandboxing for AI agents with zero cloud API dependency.
Run PrismAudio, the 518M parameter Video-to-Audio model from ICLR 2026, on a JarvisLabs A100 GPU. From setup to Gradio web UI in under 15 minutes.
A step-by-step guide to running ComfyUI workflows on cloud GPUs using the JarvisLabs CLI and Claude Code. Generate videos from photos, music from text, and 3D models from images — across multiple GPUs in parallel — all without leaving your terminal.
Introducing jl, a command-line interface for the JarvisLabs GPU cloud built for both humans and coding agents. Provision GPUs, run training jobs, monitor experiments, and let your agents handle the infrastructure.
From 8 seconds to 1.8 — how we tore apart every layer of our instance creation pipeline in three days to make GPU launches feel instant.
Learn how to scale LLM inference using data parallelism, pipeline parallelism, and tensor parallelism in vLLM. Practical guide with A100 GPU benchmarks comparing DP vs PP vs TP.
Learn 5 practical vLLM optimization methods: prefix caching, FP8 KV-cache, CPU offloading, disaggregated prefill/decode, and zero-reload sleep mode, with benchmark-backed guidance.
Part 1 of my LLM optimization research series. Exploring how Meta's disaggregated prefill-decode strategy separates prompt processing from token generation - and what it means for JarvisLabs.
Complete guide to LLM quantization with vLLM. Compare AWQ, GPTQ, Marlin, GGUF, and BitsandBytes with real benchmarks on Qwen2.5-32B using H200 GPU - 4-bit quantization tested for perplexity, HumanEval accuracy, and inference speed.
Learn how to deploy MiniMax M2.1 with vLLM for agentic workloads and coding assistants. Covers hardware requirements, tensor/expert parallelism, benchmarking on InstructCoder, tool calling with interleaved thinking, and integration with Claude Code, Cline, and Cursor.
A deep dive into CUDA cores, Tensor Cores, precision modes, and other specialized GPU features that impact performance.
Learn how to improve your Stable Diffusion prompts using Ollama and LLMs in ComfyUI. Step-by-step guide to setup, workflow, and best practices for enhanced AI image generation.
Learn how to effectively track and manage ML experiments using Weights & Biases (W&B) and Hydra. A comprehensive guide for machine learning practitioners and researchers.
Step-by-step guide to set up and run FLUX.1 Schnell for AI image generation using ComfyUI on cloud GPUs. Includes workflows, LoRA integration, and practical examples.
Explore uncensored LLM models, their differences from ChatGPT, and how they're built. Learn about foundation models, fine-tuning, and running unfiltered AI models locally.
Step-by-step guide to install and run Flux.1 AI image generator on cloud GPU. Learn how to generate high-quality AI images using Flux's open-source model with detailed setup instructions and examples.
Step-by-step guide to creating custom AI training datasets using Fooocus's face swap and pose matching features for Stable Diffusion model finetuning
Learn how to effectively deploy and interact with Ollama LLM models using terminal commands, local clients, and REST APIs. Discover tips for choosing the right GPU, managing storage, and troubleshooting common issues.
Discover how to enhance your PyTorch scripts using Hugging Face Accelerate for efficient multi-GPU and mixed precision training. Learn setup, configuration, and code adaptation for faster deep learning model training.
Learn how to train large language models efficiently using DeepSpeed and HuggingFace Trainer. This step-by-step guide shows you how to optimize GPU memory and train 10B+ parameter models on a single GPU using ZeRO-Offload.
Learn how to build a toxic comment classifier using RoBERTa and PyTorch Lightning. This step-by-step tutorial covers mixed precision training, multi-GPU setup, and Weights & Biases integration for ML model tracking.
Learn how to boost ResNet-50 accuracy from 75.3% to 80.4% using advanced training techniques, including BCE loss, data augmentation, and optimization strategies. A comprehensive guide to modern CNN training best practices.