Found 7 posts with this tag
AirLLM lets a 4GB GPU run a 70B parameter model by streaming layers from disk instead of loading them into VRAM. Here's how it works, real model-to-VRAM numbers, and the speed tradeoff nobody mentions in the headline.
What it takes to run DeepSeek V4 Flash locally: memory needs per quantization, realistic setups from a single 24GB GPU to multi-GPU rigs, and when cloud makes more sense.
Looking for a Jarvis Labs alternative? Compare RunPod, Vast.ai, Lambda Labs, and Paperspace on price per H100 hour, notebook UX, team features, and GPU availability for deep learning in 2026.
What it takes to run GLM-5.2 locally: memory needs per quantization, realistic setups from Mac Studio to multi-GPU rigs, and when cloud makes more sense.
GGUF vs GPTQ vs AWQ quantization for local LLMs explained. Which format to use with Ollama, llama.cpp, and vLLM, and how much quality you lose.
Exact RAM requirements for running LLMs locally with Ollama, llama.cpp, and LM Studio. Covers 7B to 70B+ models, CPU offloading, context windows, and DDR5 vs DDR4.
Hardware requirements for running Llama 4 Scout (109B) and Maverick (400B) locally. VRAM needs, quantization, and GPU picks for every budget.