Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference

this is about squeezing every last bit of performance out of our expensive GPUs. vLLM and TRT-LLM are necessary because running large models isn't just about loading them; it's about how you manage the KV cache and memory allocation during inference. without multi-backend support, we're just wasting cycles waiting for GPU synchronization.

this means we can finally move past running large models slowly on standard PyTorch and start exploiting the specialized acceleration features of NVIDIA hardware. we're talking about minimizing memory fragmentation and maximizing throughput, which directly impacts the cost per token. that's where the real money is.

it's not a silver bullet, but it's the necessary infrastructure layer. we need these tools to manage the sheer memory demands of serving state-of-the-art LLMs economically, not just run them.