The primary limiting factor for running large language models (LLMs) like Llama 3.3 70B is the amount of available VRAM on your GPU. Llama 3.3 70B, in its FP16 (half-precision floating point) format, requires approximately 140GB of VRAM to load the model weights. The NVIDIA RTX 3080 10GB only provides 10GB of VRAM, resulting in a significant shortfall of 130GB. This means the model cannot be loaded into the GPU's memory in its native FP16 format. Memory bandwidth, while important for overall performance, becomes secondary when the model cannot even fit within the available memory. The RTX 3080's 0.76 TB/s memory bandwidth would be sufficient *if* the model could fit.
Without sufficient VRAM, attempting to run the model directly will result in an out-of-memory error. While techniques like CPU offloading exist, they introduce significant performance bottlenecks due to the slower data transfer rates between the GPU and system RAM. This dramatically reduces inference speed, making real-time or interactive applications impractical. The number of CUDA and Tensor cores, while contributing to computational throughput, cannot compensate for the fundamental limitation imposed by insufficient VRAM. The model's context length of 128,000 tokens is also irrelevant if the base model cannot be loaded.
Given the severe VRAM limitation, directly running Llama 3.3 70B on an RTX 3080 10GB is not feasible without substantial compromises. The most practical approach is to explore aggressive quantization techniques. Quantization reduces the memory footprint of the model by representing weights with fewer bits. For example, using 4-bit quantization (Q4) could potentially bring the VRAM requirement down to approximately 35GB, still exceeding the 3080's capacity, but opening the door for CPU offloading or splitting the model across multiple GPUs if available. Consider using llama.cpp or similar frameworks that specialize in efficient quantization and CPU/GPU offloading. Alternatively, explore smaller models, such as Llama 3 8B or similar models with fewer parameters that can fit within the RTX 3080's VRAM.