AirLLM 70B inference with single 4GB GPU

What changed

Quickstart | Configurations | MacOS | Example notebooks | FAQ AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. You can even run 405B Llama 3.1 on 8GB, DeepSeek-V3 (671B) on ~12GB, and Kimi K3 (2.8T) — the largest open-source model released to date — on under 4GB, because sparse MoE models stream one expert at a time rather than a whole layer. - Best AI Game Sprite Generator - Best AI Facial Expression Editor - Bloome — build & run AI agent teams in the cloud, zero setup [2026/07] Kimi K3 (2.8T) support: the largest open-source model runs on a single card in 3.72GB of VRAM, measured end to end on one RTX 6000 Ada.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Running a 28.9M parameter LLM on an $8 microcontroller
• A $500 RL fine-tune of a 9B open model beat frontier models on catalog review
• Nvidia, Microsoft, Meta warn against overregulating open-weight models

• Running a 28.9M parameter LLM on an $8 microcontroller similarity 0.57
• A $500 RL fine-tune of a 9B open model beat frontier models on catalog review similarity 0.51
• Nvidia, Microsoft, Meta warn against overregulating open-weight models similarity 0.43