The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3.1 8B model, especially in its Q4_K_M (4-bit) quantized form. The quantized model requires approximately 4GB of VRAM, leaving a substantial 20GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths, improving throughput and enabling more complex AI tasks. The RTX 3090 Ti's high memory bandwidth (1.01 TB/s) ensures rapid data transfer between the GPU and memory, crucial for minimizing latency during inference. Furthermore, the 10752 CUDA cores and 336 Tensor Cores accelerate the matrix multiplications and other computations inherent in running large language models like Llama 3.1 8B.
For optimal performance, leverage the available VRAM by experimenting with larger batch sizes, potentially up to 12, to maximize throughput. Utilize a context length of 128000 tokens to take full advantage of the model's capabilities. If you encounter performance bottlenecks, consider using a more optimized inference framework like `llama.cpp` with CUDA support, or `vLLM` for higher throughput. If you need to reduce VRAM usage further, explore even more aggressive quantization methods, but be aware that this can impact model accuracy.