The NVIDIA RTX 3090, while a powerful GPU, falls short of the VRAM requirements for directly running the Llama 3 70B model in FP16 (half-precision). Llama 3 70B demands approximately 140GB of VRAM in FP16, whereas the RTX 3090 only offers 24GB. This significant shortfall (-116GB) means the model cannot be loaded entirely onto the GPU's memory, leading to out-of-memory errors if attempted. The RTX 3090's memory bandwidth of 0.94 TB/s, CUDA cores (10496), and Tensor Cores (328) are substantial, but these resources become irrelevant if the model's data cannot reside within the GPU's VRAM.
Without sufficient VRAM, the system would need to resort to techniques like offloading layers to system RAM or disk, which introduces substantial latency. This severely impacts inference speed, making real-time or interactive applications impractical. The high TDP of 350W is also a factor to consider, as pushing the GPU to its limits for potentially slow and unstable operation may lead to thermal issues. Therefore, running the full Llama 3 70B model on a single RTX 3090 without significant optimization is not feasible.
To run Llama 3 70B on an RTX 3090, you'll need to employ aggressive quantization techniques. Consider using 4-bit quantization (Q4_K_M or similar) with `llama.cpp` or `AutoGPTQ`. This will drastically reduce the VRAM footprint of the model, potentially bringing it within the RTX 3090's 24GB limit. However, expect a performance trade-off; quantization reduces accuracy, though the impact can be minimized with careful selection of the quantization method.
Alternatively, explore distributed inference across multiple GPUs if available. If you only have the RTX 3090, consider using a smaller model variant (e.g., Llama 3 8B) or offloading some layers to CPU RAM. Be prepared for significantly slower inference speeds if offloading to CPU. Carefully monitor VRAM usage during inference and adjust batch size and context length to avoid exceeding the GPU's memory capacity.