面向大语言模型的高吞吐、低显存推理与服务引擎,通过 PagedAttention 等优化显著提升并发吞吐并降低显存占用,适合部署在线推理服务。
高性能大模型与多模态模型推理服务框架,通过优化注意力机制和调度策略提升吞吐与延迟表现,支持 Llama、DeepSeek、MoE 等主流模型部署。
基于 TensorRT FP16 加速的 PP-OCRv6 推理服务,在 OmnidocBench 上跑出 200+ img/s,提供 HTTP 和 gRPC 接口,适合高吞吐文档解析与 RAG 场景。
给 vLLM 加一个免校准的 KV-cache 量化后端,只需一个开关就能把上下文拉长 3~5 倍,吞吐超过 FP16 且精度基本不掉,适合跑长上下文 Agent。
基于 C++ 和 TensorRT 的 RF-DETR 推理引擎,支持目标检测与实例分割,可在 NVIDIA GPU 和 Jetson 上以 FP32/FP16/INT8 量化部署。
…tation (segment-everything) converted to Core ML, FP16. FastSAM-s (light, ~23 MB) and FastSAM-x (quality, ~138 MB). YOLOv8-seg architecture; outputs boxes + scores + mask
…etry estimation. Single CoreML mlpackage (~200 MB FP16, 504×504 fixed input). Predicts metric depth, surface normals, and confidence mask from a DINOv2 ViT-B/14 backbone.
…tps://github.com/huggingface/accelerate/pull/3688 FP16/BF16 Training for MPS devices BF16 and FP16 support for MPS devices is finally here. You can now pass mixed precisi
What's Changed fix sage fp16 triton repeat by @ITerydh in https://github.com/feifeibear/long-context-attention/pull/146 bump to 0.6.3.post1 by @feifeibear in https://gith
…0.onnx 326 MB English, full precision kokoro-v1.0.fp16.onnx 164 MB spectral correlation 0.999 against fp32 kokoro-v1.0.int8.onnx 114 MB spectral correlation 0.916 against
…rchu/kokoro-onnx/releases/tag/v0.2.0) kokoro-v1.0.fp16.onnx : (169MB): optimized f16 version from [taylorchu/kokoro-onnx](https://github.com/taylorchu/kokoro-onnx/release