The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is a powerful GPU suitable for many AI tasks. However, running a large language model like Llama 3.1 70B, even in a quantized form, presents a challenge due to its substantial memory footprint. While the Q4_K_M quantization reduces the model's VRAM requirement to approximately 35GB, this still exceeds the RTX 4090's available 24GB. This VRAM shortfall means the entire model cannot reside on the GPU, leading to out-of-memory errors or severely degraded performance as data is swapped between system RAM and GPU memory.
Even with the RTX 4090's impressive 16384 CUDA cores and 512 Tensor cores, the inability to fit the entire model in VRAM will drastically limit inference speed. The constant data transfer between system RAM and VRAM, known as offloading, introduces significant latency, negating much of the GPU's computational power. While the 1.01 TB/s memory bandwidth is high, it cannot compensate for the sheer volume of data that needs to be moved, resulting in a poor user experience.
Unfortunately, running Llama 3.1 70B (70.00B) with Q4_K_M quantization on a single RTX 4090 is not feasible due to insufficient VRAM. Consider using a smaller model variant, such as a Llama 3.1 8B or 15B, which would fit comfortably within the 24GB VRAM. Alternatively, explore distributed inference solutions that allow you to split the model across multiple GPUs. If you must run the 70B model, consider using CPU inference, although this will be significantly slower than GPU inference. Another option is to explore cloud-based solutions that offer GPUs with larger VRAM capacities.
If you choose to run a smaller model, llama.cpp with appropriate quantization is a good starting point. For larger models that can fit (or nearly fit) into VRAM, explore vLLM or text-generation-inference for optimized performance. These frameworks offer advanced techniques like continuous batching and tensor parallelism, which can significantly improve throughput and reduce latency.