“Fast LLM Inference From Scratch” 从零开始构建一个大语言模型(LLM)推理引擎andrewkchan.dev/posts/yalm.html本文介绍了从零开始构建一个大语言模型(LLM)推理引擎的过程,使用C++和CUDA实现,不依赖外部库。作者通过逐步优化,从CPU单线程实现到GPU加速,最终实现了接近行业顶尖水平的推理速
可以一读↓ Fast LLM Inference From Scratch 从头开始进行快速 LLM 推理无需库即提升单 GPU 推理吞吐能力#ai创造营##chatgpt# 访问:andrewkchan.dev/posts/yalm.html #ChatGPT[超话]#
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@@ -50,11 +50,12 @@ Once installed, you can import code from any chapter using: 50 50 from llms_from_scratch.ch02 import GPTDatasetV1, create_dataloader_v1 51 51 52 52 from llms_from_scratch.ch03 import ( 53 - MultiHeadAttention, 54 53 SelfAttention_v1, 55 54 SelfAtt...
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding. Resources Read...
FlashAttention-3 achieves up to 75% GPU utilization on H100s, making AI models up to 2x faster and enabling efficient processing of longer text inputs. It allows for faster training and inference of LLMs, supports lower precision operations for improved efficiency. ...
A pose-estimation model that supports real-time inference on edge with 9x faster inference performance than the OpenPose model. PeopleSemSegNet, a semantic segmentation network for people detection. A variety of computer vision pretrained models in various industry use cases, such as license plate de...
What is a generative large language model from a technical perspective? A generative LLM is a function. It takes a text string as input (called "prompt" in AI parlance), and returns an array of strings and numbers. Here's what the signature of this function looks like: ...
Foundation models have immense compute requirements for training and inference, requiring large volumes of specialized hardware. That is a significant contributor to the high costs and operational constraints (throughput and concurrency) that application developers face. The largest players can find the cas...
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding. Resources Read...