The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3.1 8B model, especially when quantized to q3_k_m. This quantization reduces the model's memory footprint to a mere 3.2GB, leaving a substantial 20.8GB of VRAM headroom. This ample VRAM allows for larger batch sizes and longer context lengths, improving throughput and enabling more complex and nuanced interactions with the model. The RTX 4090's high memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and VRAM, further enhancing performance. The 16384 CUDA cores and 512 Tensor Cores are leveraged for parallel processing and optimized matrix multiplication, crucial for efficient inference.
Given the RTX 4090's capabilities and the model's relatively small footprint after quantization, users should experiment with larger batch sizes to maximize throughput. A batch size of 13 is a good starting point, but increasing it further may yield even better performance without exceeding VRAM limits. Utilizing inference frameworks like `llama.cpp` or `vLLM` can provide additional optimizations and hardware acceleration. Monitor GPU utilization and memory consumption to fine-tune settings for optimal performance. Consider using a lower quantization level if you need higher accuracy and have enough VRAM, but for most use cases, q3_k_m provides a great balance between performance and accuracy.