NVIDIA Llama-3 Nemotron Super 49B GGUF
| Property | Value |
|---|---|
| Original Model | NVIDIA Llama-3 Nemotron Super 49B |
| Quantization Framework | llama.cpp (b4915) |
| Size Range | 13.66GB - 99.74GB |
| Model URL | https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1 |
What is nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF?
This is a comprehensive collection of quantized versions of NVIDIA's 49B parameter language model, optimized for different deployment scenarios. The quantizations range from extremely high quality (Q8_0) to very compressed versions (IQ2_XXS), enabling deployment across various hardware configurations.
Implementation Details
The model uses llama.cpp's advanced quantization techniques, including both traditional K-quants and newer I-quants. Each version is calibrated using a specialized imatrix dataset, offering different balances between model size and performance.
- Multiple quantization formats (Q8_0 to IQ2_XXS)
- Special handling of embedding/output weights in certain versions
- Support for online weight repacking for ARM and AVX architectures
- Optimized prompt format with system, user, and assistant markers
Core Capabilities
- High-quality text generation with varying compression ratios
- Efficient deployment options for different hardware configurations
- Special optimizations for ARM and AVX systems
- Support for both CPU and GPU inference
Frequently Asked Questions
Q: What makes this model unique?
This model offers an exceptionally wide range of quantization options for a large 49B parameter model, making it accessible for deployment on hardware ranging from high-end servers to more modest systems. The innovative use of both K-quants and I-quants provides users with optimal choices for their specific use cases.
Q: What are the recommended use cases?
For maximum quality, use Q6_K or higher quantizations. For balanced performance, Q4_K_M is recommended as the default choice. For systems with limited RAM, the I-quants (IQ3_M and below) offer surprisingly good performance at smaller sizes. GPU users should consider K-quants for Vulkan/AMD or I-quants for NVIDIA/ROCm deployments.
