The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3 8B model, especially when using quantization. The Q4_K_M quantization reduces the model's VRAM footprint to approximately 4GB, leaving a substantial 20GB of headroom for larger context lengths, batch processing, and other concurrent tasks. The RTX 3090's high memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference. The 10496 CUDA cores and 328 Tensor Cores further accelerate computations, leading to faster token generation.
The Ampere architecture of the RTX 3090 is optimized for AI workloads, providing efficient matrix multiplication operations that are crucial for transformer-based models like Llama 3. The estimated 72 tokens/sec performance indicates real-time or near-real-time text generation capabilities. A batch size of 12 allows for processing multiple requests simultaneously, increasing overall throughput. This combination of ample VRAM, high memory bandwidth, and powerful compute cores makes the RTX 3090 an excellent choice for deploying Llama 3 8B.
Given the ample VRAM headroom, experiment with increasing the context length towards the model's maximum of 8192 tokens to improve the model's ability to handle longer and more complex prompts. Utilize the available VRAM to increase the batch size to improve throughput. Consider using inference frameworks like `llama.cpp` for CPU+GPU inference or `vLLM` for optimized GPU-only inference. Monitor GPU utilization and temperature to ensure optimal performance and prevent overheating, especially during extended use. If you encounter performance issues, try different quantization methods or reduce the context length and batch size.