Summary of Reddit Comments for "llama 3 2 without gpu":

Missing GPU in llama 3.2

• Users shared experiences using ollama for embedding and koboldcpp for inference without GPU.
• One user mentioned: "Unfortunately, I am CPU bound. This lappy has no GPU unless you call its intel HD with 2GB vram an actual gpu!"
• The llama 3.2 model was run successfully on a system with 8GB of VRAM.
• One user raised a question: "But can it run on a pi5?"
• Another user highlighted the fan noise difference between different GPU manufacturers.

Performance Comparisons with GPUs

• Discussion involved performance statistics with different GPUs like RTX 3090 and RTX 3060.
• Users shared experiences with running models on CPU, different GPU setups, and the impact on performance.
• llama.cpp was mentioned as not dependent on Python, only requiring CUDA.

Efficiencies and Model Sizes

• A discussion focused on model quantization and the impact on model sizes.
• Exllama2 and llama.cpp were compared regarding handling the quantization of models.
• Users shared experiences regarding the fit of different LLM models and quantization methods on GPUs.

Use Cases and Tools

• Users shared experiences of training models, tackling incorrect model responses, and preparing data for fine-tuning LLaMA models.
• Various software tools like deep coder v2.1 and llama.cpp were suggested for optimizing performance.
• The need for Vulkan support and discussions on alternate tools like cortex.cpp were brought up.
• Jan.AI and lmstudio were mentioned as alternatives with unique features for LLM models.

Llama Models and Model Management

• Users discussed downloading, managing, and using various Llama models.
• ollama and cortex.cpp were compared regarding their features and suitability for different users.
• Issues related to financial models, downloads, multi-part ggufs, and compatibility with other LLM models like text-to-image models were raised.
• Users shared experiences of model usage, model downloading, and offered feedback on the usability and features of different tools.

Technical Details and Applications

• Users discussed technical aspects of model handling, model downloading, and model compatibility.
• The possibility of using text-to-image models and suggestions for improvement in model management were highlighted.
• ollama and other tools were compared in terms of features, functionality, and usability for different LLM applications.

These Reddit comments provide a comprehensive look at experiences, comparisons, and discussions surrounding llama 3.2 models without GPU, GPU performance, model sizes, software tools, and technical details related to LLM applications.