browsing web:

• when the model doesn't fit on the gpu ram, you loose 10x the gpu performance.. making APU with big RAM competing with big RTX3090
• hardware:
  • the models must fit inside the RAM or you loose 10x the performance
  • RAM is even more crucial for reasoning models who needs to remember
  • big usefull model seems to require between 16 to 32 Go RAM, so:
    • a new pc apu + npu with 64Go RAM is the caution
    • if a gpu, the ratio CPU ram = 4x GPU ram seems still nice, so 64gb ram CPU + option a 16gb GPU
    • unified RAM makes big APU with big bandwith the "cheap" competitive idea in 2025
  • bandwith to RAM is important: 256 bit bus looks minimum, 384 lovely
  • 1T disk may be too small
• evolution pressure:
  • Microsoft "copilote" ==> npu and ram
  • electricity ==> APU seems 10x more efficient then gpu
  • mac-mini style pc the low cost choice, ep if hardware renewal strategy every 2 years
  • token cost is trending -20x per year:
    • so there is no reason sink too much money on hardware, renew more often, but check minimums
    • apple M4 are the reference: we can only hope from competition from Qualcomm, AMD, Nvidia

asking to AI (gemini flash 2):

• software:
  • llama.cpp:ggml-org/llama.cpp , greatest but first oriented Mac / Arm
  • ollama: ollama.ai:ollama.ai
  • LM studio:https://lmstudio.ai/
  • torch / Tensorflow / Jax ? not really for cpu
  • intel IPEX-LLM for optimizing on cpu (works below Torch?)
  • ... docker models are looking like a trend

*** gemini flash 2: 2025-03-16

It's great you're looking into running LLMs locally! Here's a breakdown of how to obtain llama.cpp, Ollama, and LM Studio:

1. llama.cpp:

• GitHub:
  • The primary source is the llama.cpp GitHub repository:
    • [ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp)
  • From there, you have a few options:
    • Building from source: This offers the most flexibility but requires technical knowledge. You'll need to compile the code according to your operating system.
    • Downloading pre-built binaries: On the github release page, you can find prebuilt binaries. Be sure to download the correct version for your CPU architecture.
• Key points:
  • llama.cpp is a C++ library, so it's primarily command-line driven.
  • It's highly optimized for CPU inference, especially with quantized models.

2. Ollama:

• Official Website:
  • The easiest way to get Ollama is from its official website:
    • [ollama.ai](https://ollama.ai/)
  • The website provides straightforward installation instructions for macOS, Linux, and Windows.
  • Installation often involves running a simple installation script.
• Key points:
  • Ollama simplifies the process of running LLMs locally.
  • It handles model management, making it user-friendly.

3. LM Studio:

• Official Website:
  • You can download LM Studio from its official website:
    • [lmstudio.ai](https://lmstudio.ai/)
  • The website provides installers for various operating systems.
• Key points:
  • LM Studio offers a graphical user interface (GUI), making it very accessible.
  • It allows you to easily download and run LLMs from Hugging Face.
  • It uses llama.cpp in the background.

General Tips:

• When downloading or installing, always prioritize official sources to ensure safety.
• Pay attention to system requirements, especially RAM and storage space.
• If you're new to LLMs, Ollama or LM Studio might be easier starting points.

I hope this helps!

simple comparisons: