✅️⭐YOU CAN RUN A 70B AI MODEL ON YOUR POTATO GPU

[271]

forget needing a $10,000 server there's an open source tool called AirLLM that lets you run full 70B parameter models  on a GPU with just 4GB VRAM 
normal LLMs need 130GB+ of VRAM to load a 70B model AirLLM figured out something insane: you don't need all 80 layers loaded at once. so instead it loads ONE layer at a time from disk, runs the computation, frees the memory, loads the next layer. peak GPU usage stays under 4GB the entire time.
it even runs Llama 3.1 405B on just 8GB VRAM.
what it supports:

• Llama 3 / 3.1 (8B, 70B, 405B)
  
• Mistral & Mixtral
  
• Qwen 2.5
  
• works on Windows, Linux, macOS (including Apple Silicon)
  
• optional 3x speed boost with block-wise compression
  

yes it's slower than normal inference layer-by-layer loading means roughly 100 seconds per token without compression, around 33 seconds with not for real-time chat 
setup is literally 3 lines:

 

Code:

[code]
pip install airllm

[/code]
 

Code:

[code]
from airllm import AutoModel model = AutoModel.from_pretrained("meta-llama/Llama-3-70b") output = model.generate("your prompt here")

[/code]

? everything you need:

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---

? Join our community for more free tools, daily drops & API key giveaways:
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CHEAP API KEYS: corvusllm.com

TELEGRAM SUPPORT: @corvusllm

DISCORD SERVER: discord.gg/fxmgdwDvbH

[56]

[font][font]Thanks you buddy[/font][/font]

[9]

forget needing a $10,000 server there's an open source tool called AirLLM that lets you run full 70B parameter models  on a GPU with just 4GB VRAM 
normal LLMs need 130GB+ of VRAM to load a 70B model AirLLM figured out something insane: you don't need all 80 layers loaded at once. so instead it loads ONE layer at a time from disk, runs the computation, frees the memory, loads the next layer. peak GPU usage stays under 4GB the entire time.
it even runs Llama 3.1 405B on just 8GB VRAM.
what it supports:

• Llama 3 / 3.1 (8B, 70B, 405B)
  
• Mistral & Mixtral
  
• Qwen 2.5
  
• works on Windows, Linux, macOS (including Apple Silicon)
  
• optional 3x speed boost with block-wise compression
  

yes it's slower than normal inference layer-by-layer loading means roughly 100 seconds per token without compression, around 33 seconds with not for real-time chat 
setup is literally 3 lines:

 

Code:

[code]
pip install airllm

 

Code:

[code]
from airllm import AutoModel model = AutoModel.from_pretrained("meta-llama/Llama-3-70b") output = model.generate("your prompt here")

[/code]

? everything you need:

 

---

? Join our community for more free tools, daily drops & API key giveaways:
? Discord: https://discord.gg/FF9zD5G7
? Telegram: https://t.me/cheapaiapikeys
[/quote]

thanks for sharing

[7]

lol, thx

[144]

thanks bro!

[122]

forget needing a $10,000 server there's an open source tool called AirLLM that lets you run full 70B parameter models  on a GPU with just 4GB VRAM 
normal LLMs need 130GB+ of VRAM to load a 70B model AirLLM figured out something insane: you don't need all 80 layers loaded at once. so instead it loads ONE layer at a time from disk, runs the computation, frees the memory, loads the next layer. peak GPU usage stays under 4GB the entire time.
it even runs Llama 3.1 405B on just 8GB VRAM.
what it supports:

• Llama 3 / 3.1 (8B, 70B, 405B)
  
• Mistral & Mixtral
  
• Qwen 2.5
  
• works on Windows, Linux, macOS (including Apple Silicon)
  
• optional 3x speed boost with block-wise compression
  

yes it's slower than normal inference layer-by-layer loading means roughly 100 seconds per token without compression, around 33 seconds with not for real-time chat 
setup is literally 3 lines:

 

Code:

[code]
pip install airllm

[/code]
 

Code:

[code]
from airllm import AutoModel model = AutoModel.from_pretrained("meta-llama/Llama-3-70b") output = model.generate("your prompt here")

[/code]

? everything you need:

 

---

? Join our community for more free tools, daily drops & API key giveaways:
? Discord: https://discord.gg/FF9zD5G7
? Telegram: https://t.me/cheapaiapikeys

Ty

[54]

yess

[305]

keep going
great job