IBM / LLM-performance-prediction
Predict the performance of LLM inference services
☆13Updated 4 months ago
Related projects ⓘ
Alternatives and complementary repositories for LLM-performance-prediction
- Cloud Native Benchmarking of Foundation Models☆20Updated 2 weeks ago
- An interference-aware scheduler for fine-grained GPU sharing☆111Updated 6 months ago
- SpotServe: Serving Generative Large Language Models on Preemptible Instances☆101Updated 9 months ago
- Artifacts for our NSDI'23 paper TGS☆68Updated 5 months ago
- A ChatGPT(GPT-3.5) & GPT-4 Workload Trace to Optimize LLM Serving Systems☆133Updated last month
- ☆14Updated 5 months ago
- Bamboo is a system for running large pipeline-parallel DNNs affordably, reliably, and efficiently using spot instances.☆47Updated last year
- Helios Traces from SenseTime☆48Updated 2 years ago
- The source code of INFless,a native serverless platform for AI inference.☆35Updated 2 years ago
- LLM serving cluster simulator☆81Updated 6 months ago
- A resilient distributed training framework☆85Updated 7 months ago
- Stateful LLM Serving☆38Updated 3 months ago
- ☆46Updated 5 months ago
- ☆37Updated 3 years ago
- Artifacts for our ASPLOS'23 paper ElasticFlow☆52Updated 6 months ago
- ☆55Updated last week
- ☆22Updated 3 years ago
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving (OSDI 23)☆78Updated last year
- Artifact of OSDI '24 paper, ”Llumnix: Dynamic Scheduling for Large Language Model Serving“☆57Updated 5 months ago
- ☆51Updated 3 years ago
- ☆41Updated last year
- Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction | A tiny model can tell you the verbosity of an LLM (…☆22Updated 5 months ago
- ☆16Updated last year
- Efficient and easy multi-instance LLM serving☆216Updated this week
- Serverless optimizations☆50Updated 8 months ago
- Official repository for the paper DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines☆14Updated 11 months ago
- ☆48Updated last year
- ☆56Updated 2 years ago
- ☆12Updated last year
- A benchmark suite for evaluating FaaS scheduler.☆22Updated 2 years ago