The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3 8B model, especially when using quantization. The Q4_K_M 4-bit quantization significantly reduces the model's VRAM footprint to approximately 4GB. This leaves a substantial 20GB VRAM headroom, ensuring that the model and its associated processes can operate comfortably without exceeding memory limits. The RTX 3090 Ti's Ampere architecture, featuring 10752 CUDA cores and 336 Tensor cores, provides ample computational power for efficient inference. The high memory bandwidth further facilitates rapid data transfer between the GPU and memory, crucial for minimizing latency during inference.
Given the ample VRAM headroom and the RTX 3090 Ti's capabilities, users should prioritize maximizing throughput and response quality. Experiment with increasing the batch size to improve tokens/sec, but monitor VRAM usage to avoid exceeding the available capacity. While Q4_K_M offers excellent memory savings, consider testing slightly higher quantization levels (e.g., Q5_K_M or Q8_0) if you observe minimal performance degradation, as this can improve output quality. Ensure you are using the latest NVIDIA drivers for optimal performance and compatibility.