hao-ai-lab / vllm-ltr
[NeurIPS 2024] Efficient LLM Scheduling by Learning to Rank
☆34Updated 2 months ago
Alternatives and similar repositories for vllm-ltr:
Users that are interested in vllm-ltr are comparing it to the libraries listed below
- ☆48Updated 7 months ago
- ☆40Updated last month
- PyTorch implementation of paper "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline".☆81Updated last year
- [OSDI'24] Serving LLM-based Applications Efficiently with Semantic Variable☆136Updated 4 months ago
- ☆82Updated 2 months ago
- 16-fold memory access reduction with nearly no loss☆72Updated 2 months ago
- ☆72Updated 2 years ago
- ☆51Updated 10 months ago
- PyTorch library for cost-effective, fast and easy serving of MoE models.☆115Updated last week
- High performance Transformer implementation in C++.☆99Updated last week
- [ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference☆236Updated 2 months ago
- Odysseus: Playground of LLM Sequence Parallelism☆64Updated 7 months ago
- nnScaler: Compiling DNN models for Parallel Training☆87Updated 3 weeks ago
- ☆70Updated 3 years ago
- A ChatGPT(GPT-3.5) & GPT-4 Workload Trace to Optimize LLM Serving Systems☆142Updated 3 months ago
- A sparse attention kernel supporting mix sparse patterns☆98Updated 3 months ago
- ☆84Updated 3 months ago
- Automated Parallelization System and Infrastructure for Multiple Ecosystems☆77Updated 2 months ago
- (NeurIPS 2022) Automatically finding good model-parallel strategies, especially for complex models and clusters.☆37Updated 2 years ago
- [ICML 2024] Serving LLMs on heterogeneous decentralized clusters.☆18Updated 8 months ago
- A resilient distributed training framework☆88Updated 9 months ago
- [ICLR2025] Breaking Throughput-Latency Trade-off for Long Sequences with Speculative Decoding☆105Updated last month
- Stateful LLM Serving☆44Updated 6 months ago
- Explore Inter-layer Expert Affinity in MoE Model Inference☆6Updated 8 months ago
- Official repository for the paper DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines☆17Updated last year
- ☆45Updated 3 weeks ago
- [NeurIPS 2024] The official implementation of "Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exitin…☆48Updated 7 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆61Updated 2 months ago
- A tiny yet powerful LLM inference system tailored for researching purpose. vLLM-equivalent performance with only 2k lines of code (2% of …☆133Updated 6 months ago
- ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction (NIPS'24)☆24Updated last month