AirLLM slashes LLM memory needs, enabling Kimi K3 on 4GB GPU

AirLLM has released updates that significantly reduce the memory requirements for running large language models, enabling powerful models to operate on consumer-grade hardware. Recent additions include support for Qwen3.8-27B, which requires only 3.33GB of VRAM, and Kimi K3 (2.8T), the largest open-source model to date, runnable on under 4GB of VRAM. These advancements are achieved through techniques like per-expert streaming, allowing models such as DeepSeek-V3 (671B) to run on approximately 12GB of VRAM. AI

IMPACT Enables running large language models on lower-spec hardware, democratizing access to advanced AI capabilities.

RANK_REASON This is an update to a tool that improves inference efficiency, not a new frontier model release or significant industry event.

Read on r/LocalLLaMA →

• AirLLM
• DeepSeek-V3
• Gemma
• Kimi k3
• Llama 3.1
• Phi-4
• Qwen2.5
• Qwen3
• Qwen3.8-27B

• infra
• model release

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