This project appears to make use of both vLLM and Inference Gateway (an official Kubernetes extension to the Gateway resource). The contributions of llm-d itself seems to mostly be a scheduling algorithm for load balancing across vLLM instances.
We plan to publish examples of multi-host inference that leverages LeaderWorkerSets - https://github.com/kubernetes-sigs/lws - which helps run ranked serving workloads across hosts. LeaderWorkerSet is how Google supports both TPU and GPU multi-host deployments - see https://github.com/kubernetes-sigs/lws/blob/main/config/samp... for an example.
Edit: Here is an example Kubernetes configuration running DeepSeek-R1 on vLLM multi-host using LeaderWorkerSet https://github.com/kubernetes-sigs/wg-serving/blob/main/serv.... This work would be integrated into llm-d.
* The "stack-centric" approach such as vLLM production stack, AIBrix, etc. These set up an entire inference stack for you including KV cache, routing, etc.
* The "pipeline-centric" approach such as NVidia Dynamo, Ray, BentoML. These give you more of an SDK so you can define inference pipelines that you can then deploy on your specific hardware.
It seems like LLM-d is the former. Is that right? What prompted you to go down that direction, instead of the direction of Dynamo?
1. Balance / schedule incoming requests to the right backend
2. Model server replicas that can run on multiple hardware topologies
3. Prefix caching hierarchy with well-tested variants for different use cases
So it's a 3-tier architecture. The biggest difference with Dynamo is that llm-d is using the inference gateway extension - https://github.com/kubernetes-sigs/gateway-api-inference-ext... - which brings Kubernetes owned APIs for managing model routing, request priority and flow control, LoRA support etc.
As I understand the Dynamo SDK it is about simplifying and helping someone get started with Dynamo on Kubernetes.
From the user set we work with (large inference deployers) that is not a high priority - they already have mature deployment opinions or a set of tools that would not compose well with something like the Dynamo SDK. Their comfort level with Kubernetes is moderate to high - either they use Kubernetes for high scale training and batch, or they are deploying to many different providers in order to get enough capacity and need a standard orchestration solution.
llm-d focuses on helping achieve efficiency dynamically at runtime based on changing traffic or workload on Kubernetes - some of the things the Dynamo SDK encodes are static and upfront and would conflict with that objective. Also, large deployers with serving typically have significant batch and training and they are looking to maximize capacity use without impacting their prod serving. That requires the orchestrator to know about both workloads at some level - which Dynamo SDK would make more difficult.
anttiharju•5mo ago
smarterclayton•5mo ago
anttiharju•5mo ago
I wonder if inference-d would be a fitting name.
smarterclayton•5mo ago
The models we support come from the model server vLLM https://docs.vllm.ai/en/latest/models/supported_models.html, which has a focus on large generative models. I don't see CLIP in the list.