The NVIDIA RTX 3090, with its substantial 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3.1 8B model, especially in its quantized form. The q3_k_m quantization brings the model's VRAM footprint down to a mere 3.2GB, leaving a generous 20.8GB of headroom. This ample VRAM allows for comfortable operation, accommodating larger batch sizes and longer context lengths without encountering memory constraints. The RTX 3090's high memory bandwidth (0.94 TB/s) ensures rapid data transfer between the GPU and memory, further enhancing performance. The 10496 CUDA cores and 328 Tensor Cores contribute to the model's efficient execution, accelerating both inference and training tasks.
Given the RTX 3090's robust specifications, the primary performance bottleneck is unlikely to be VRAM or memory bandwidth. Instead, the limiting factor will likely be compute throughput and the efficiency of the chosen inference framework. The estimated tokens/sec rate of 72 is a good starting point, but this can be significantly improved with optimized software and settings. The model's 8 billion parameters, while substantial, are well within the capabilities of the RTX 3090, particularly with quantization reducing the computational load. The context length of 128000 tokens is also comfortably handled by the available VRAM, allowing for processing of lengthy sequences and complex prompts.
To maximize performance, it's recommended to use an optimized inference framework like llama.cpp with appropriate CUDA support, vLLM, or NVIDIA's TensorRT. Experiment with different batch sizes to find the optimal balance between throughput and latency; a batch size of 13 is a good starting point but may be increased depending on your application. Also, consider using techniques like speculative decoding or attention optimization to further improve the tokens/sec rate. Monitor GPU utilization and memory usage to identify any potential bottlenecks and adjust settings accordingly.
If you encounter performance limitations, consider further quantization (e.g., q4_k_m or even smaller) to reduce the model's memory footprint and increase throughput, although this may come at a slight accuracy cost. Always validate the model's output after quantization to ensure acceptable quality. For real-time applications, prioritize low latency by minimizing batch size and optimizing inference pipeline.