PyPI · crates.io
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Apache-2.016 sept 2026 · métricas 2.10.0
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#moe
Todos los repositorios del registro público que llevan esta etiqueta, procedente de sus topics de GitHub o de las palabras clave que publican sus registros de paquetes. La salud se mide con la misma metodología versionada que el resto del registro.
16 registros
PyPI
NVIDIA/TensorRT-LLM
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
Licencia propia21 oct 2026 · métricas 2.10.0
PyPI · crates.io
sgl-project/sglang
SGLang is a high-performance serving framework for large language models and multimodal models.
Apache-2.016 sept 2026 · métricas 2.10.0
PyPI
flashinfer-ai/flashinfer
FlashInfer: Kernel Library for LLM Serving
Apache-2.027 ago 2026 · métricas 2.10.0
PyPI
NVIDIA/cudnn-frontend
cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.
Apache-2.012 sept 2026 · métricas 2.10.0
PyPI
modelscope/ms-swift
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Apache-2.027 ago 2026 · métricas 2.10.0
PyPI
ModelCloud/GPTQModel
LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.
Licencia propia10 sept 2026 · métricas 2.10.0
PyPI
manjunathshiva/turboquant-mlx
Extreme weight + KV cache compression for LLMs on Apple Silicon (MLX implementation of Google's TurboQuant)
Apache-2.020 oct 2026 · métricas 2.10.0
PyPI
pjordanandrsn/experts4bit-qlora
Train and serve MoE models that do not fit in VRAM: fused 4-bit experts, QLoRA, CPU/NVMe offload, and fast inference on consumer NVIDIA GPUs.
MIT15 sept 2026 · métricas 2.10.0
PyPI · crates.io
FedericoTs/quantprobe
Run a 110B on a 2016 PC with 16 GB RAM. Know your tok/s before you download. Placement beats budget: predicts speed + memory fit for any GGUF on your exact hardware, self-calibrates, emits the exact llama.cpp command — or 'quantprobe auto' does it all. Falsification-tested laws; misses published at full size. pip install quantprobe
MIT19 oct 2026 · métricas 2.10.0
PyPI
FlashML-org/FreeToken
FreeToken brings datacenter-scale model serving to your desktop. Run massive models locally, fast and efficiently.
Apache-2.010 sept 2026 · métricas 2.10.0
PyPI
pjordanandrsn/grouped-nf4-gemm
Triton kernels for 4-bit MoE inference: grouped NF4/MXFP4 GEMM, INT4 GEMV, FP8 paged attention, and CPU/NVMe expert streaming.
MIT24 sept 2026 · métricas 2.10.0
Go
townsendmerino/goinfer
Pure-Go, no-cgo local LLM inference — run Gemma, Qwen, Llama and friends from safetensors or GGUF in a single static binary.
MIT19 oct 2026 · métricas 2.10.0
PyPI
RobTand/gridbook
Out-of-tree vLLM plugin and open format spec for NVFP4-CB / FP8-CB product-codebook weights — 2-6 bit-per-weight LLM quantization served on native Blackwell tensor cores.
Apache-2.018 sept 2026 · métricas 2.10.0
PyPI
jjang-ai/jangq
JANG — GGUF for MLX. YOU MUST USE JANG_Q RUNTIME. Adaptive Mixed-Precision Quantization + Runtime for Apple Silicon
Sin licencia13 sept 2026 · métricas 2.10.0
PyPI
ChadRoesler/MsMoEMaker
El repositorio no publica descripción.
AGPL-3.018 sept 2026 · métricas 2.10.0