The primary limiting factor in running large language models (LLMs) like LLaVA 1.6 34B is VRAM. This model, in FP16 precision, requires approximately 68GB of VRAM to load and operate. The NVIDIA RTX 3060, while a capable card, only provides 12GB of VRAM. This creates a significant shortfall of 56GB, preventing the model from even being loaded onto the GPU for inference in its native FP16 format. The RTX 3060's memory bandwidth of 0.36 TB/s, while decent, becomes largely irrelevant as the model cannot fit within the available memory.
Beyond VRAM, the number of CUDA and Tensor cores also impacts performance. The RTX 3060's 3584 CUDA cores and 112 Tensor cores will provide reasonable acceleration for smaller models that fit within its memory capacity. However, with LLaVA 1.6 34B, even if VRAM limitations were bypassed, the model's size would likely result in very slow inference speeds due to the intensive computations required. The Ampere architecture is generally efficient, but it can't overcome the fundamental memory constraint in this scenario.
Unfortunately, running LLaVA 1.6 34B directly on an RTX 3060 12GB is not feasible due to the massive VRAM requirement. To potentially run a model of this scale, you would need to explore extreme quantization techniques or distributed inference across multiple GPUs. However, even with aggressive quantization, performance will likely be severely degraded. A more practical approach would be to consider using a smaller model that fits within the RTX 3060's VRAM, or to leverage cloud-based inference services that offer access to GPUs with sufficient memory.
Alternatively, investigate CPU-based inference using llama.cpp with very aggressive quantization (e.g., 4-bit or lower). This will be significantly slower than GPU inference, but it might allow you to experiment with the model at a reduced scale. Another option is to explore cloud-based solutions or renting a more powerful GPU instance with sufficient VRAM.