[GH-ISSUE #1035] [RoadMap][Call For Contributions] Mooncake Store V3 Roadmap

bsakaguchi commented

2026-10-29 21:13:00 +00:00

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Originally created by @stmatengss on GitHub (Nov 7, 2025).
Original GitHub issue: https://github.com/kvcache-ai/Mooncake/issues/1035

Milestone 1: Core Architecture Refactor & Decoupling

This milestone focuses on foundational architectural changes to improve modularity, flexibility, and prepare for future scaling.

(TE/Store Separation): Decouple the TE (Task/Tensor Engine) and Store components into separate, independent packages.
(Client/Worker Decoupling): Decouple the dummy client from the worker to remove strong dependencies. @YiXR
(Flexible Deployment): Update the Store to support various flexible deployment models, such as client-only, client + master, etc.
(Tensor-native APIs): Put/Get Tensor APIs contain TP rank and model info.
- #1127
(Multiple Replicas): Support a replica list for each KV item.
- #1148
(Layer-wise Storage): Maintaining layer info on the Mooncake side, supports stream transfer and communication overlap.

Milestone 2: Master Service Enhancements

This milestone enhances the Master component to support new storage architectures and routing logic.

(Key-based Routing): Implement new key-based routing capabilities in the Master service.
(Metadata Adaptation - Storage): Adapt the Master's metadata management to support the new multi-level storage architecture.
(Recovery) kv metadata persistency https://github.com/kvcache-ai/Mooncake/pull/1451
(KVCache Awareness Interface) Exposes hit ratio for different layers.
(Metadata Adaptation - HA): Upgrade metadata schema and logic to meet new High Availability (HA) requirements.
(Multi-tenant): Support Multi-tenant with different models, users and auth keys

Milestone 3: Worker: Multi-Level Storage Architecture

This is a major epic to build the next-generation multi-level storage system within the Worker.

3.1: Abstraction & Caching
- (Storage Abstraction Layer): Design and implement the core abstraction layer for multi-level storage.
- (Cache Scheduling Interface): Design the abstract interface for cache scheduling logic.
- (Eviction Logic): Implement basic data eviction logic within the new storage architecture.
  - @Vincent-Bo-ali #1028
- (LRU Cache): Implement an LRU (Least Recently Used) policy as the default cache scheduling strategy.
- (Local Client Cache): Keep a local cache for better performance.
  - #1062
3.2: Storage Backend Implementation
- (DRAM Adaptation): Adapt the storage layer for DRAM, including support for NUMA affinity.
- (SSD Adaptation): Adapt the storage layer for SSDs, enabling local external storage read/write capabilities.
  - #968
  - #1031
  - #1054
  - #1203
  - #1284
  - #1319
  - #1456
- (VRAM Adaptation): Adapt the storage layer to utilize VRAM.
- (Huawei NPU Adaptation): Implement support for Huawei NPUs (H2D).
  - @ascend-direct-dev
3.3: Elastic KVCache Storage
- (KVCache Migration): Move KVCache between Mooncake client.
  - #1326
- (Data Replica): Dynamic Replication
  - #1100

Milestone 4: Worker: Networking & Elasticity

This milestone focuses on refactoring worker communication and enabling resource elasticity.

(RPC Refactor): [Phase 1] Refactor the worker's read/write logic to replace RDMA with RPC-based communication.
(Barex Transport Support): Support Alibaba barex transport in TE for Mooncake Store.
- #1045
(Resource Elasticity): Implement single-worker resource elasticity.
(Event‑driven completion): Provide an option to using event-driven notification worker instead of busy-polling.
- #1033
- #1053
(IPv6 Support): Support IPv6 in client, master and metadata server.
- #1043
- #1067

Milestone 5: Deployment & Operations

This milestone covers K8s integration (i.e., RBG, https://github.com/sgl-project/rbg) and build process improvements.

(K8s Autoscaling): Implement support for Kubernetes-based autoscaling of worker and dummy client instances.
(Scenario-based Builds): Implement a build system capable of producing different worker binaries optimized for different scenarios.
(Integration With AI Configurator): Use AI Configurator for better measuring Resource workers and other configurations.
(Deployment Documentation & Guides): Create comprehensive, up-to-date deployment documentation and step-by-step setup guides to simplify installation and configuration for all environments.

Milestone 6: Conductor

This feature implements a standalone or co-located conductor for use by the Gateway. @zhongzhouTan-coder @yejj710 @Liziqi-77 @Keithwwa @Asher-XunZhang

(Basic design) : Discuss and design the standard specifications for KV indexer and kv-events
- https://github.com/kvcache-ai/Mooncake/issues/1403
- https://github.com/kvcache-ai/Mooncake/issues/1527
(KV indexer): Implement a Mooncake KV-Store Indexer service (include /quey, /query_by_hash ..etc)
(KV publisher): support kv-event publisher in Mooncake store
(Service registration): support two methods for service registration: either through static configuration files or dynamic service registration APIs
(KV events): Support consuming KV events generated by inference frameworks and Mooncake

Milestone 7: Hardware Compatibility

(AMD) HIP, ROCm, MORI
- #1154
- #1179
- #1189
(Moore Thread)
- #973
- #1176
(AWS EFA)
- #1509
- #1523
- #1526

Milestone 8: Pytorch Eco-system

(Support Tensor Attributions)
(Native torch format offload)

Milestone 9: CI & CD enhancement

(End-to-end CI tests): For SGLang, support Hicache, PD, Elatics EP, checkpoint engine tests.
- SGLang PD E2E CI: #1089
- Mooncake EP CI: #1099
- SGLang HiCache E2E CI: #1181
- vLLM 1P1D E2E CI: #1491

Milestone 10: Performance & Benchmarks

(Store Master Benchmark): Design and integrate a dedicated benchmark for the Mooncake store master module to evaluate throughput, latency, and scalability. #1201
(SSD/NVMe storage benchmark) : #1388

Milestone 11: RL Infra

(ROLL):
(Slime/Miles) :
- https://github.com/THUDM/slime/pull/1709
- https://github.com/radixark/miles/pull/591

Thanks for being a part of the Mooncake community! Welcome to discuss and contribute!

If you have any ideas, just leave a comment below and help shape the Roadmap.

Originally created by @stmatengss on GitHub (Nov 7, 2025). Original GitHub issue: https://github.com/kvcache-ai/Mooncake/issues/1035 ### Milestone 1: Core Architecture Refactor & Decoupling This milestone focuses on foundational architectural changes to improve modularity, flexibility, and prepare for future scaling. - [ ] (TE/Store Separation): Decouple the TE (Task/Tensor Engine) and Store components into separate, independent packages. - [x] (Client/Worker Decoupling): Decouple the dummy client from the worker to remove strong dependencies. @YiXR - https://github.com/kvcache-ai/Mooncake/pull/1084 - https://github.com/kvcache-ai/Mooncake/pull/1122 - #1146 - https://github.com/kvcache-ai/Mooncake/pull/1177 - https://github.com/kvcache-ai/Mooncake/pull/1206 - [ ] (Flexible Deployment): Update the Store to support various flexible deployment models, such as client-only, client + master, etc. - [ ] (Tensor-native APIs): Put/Get Tensor APIs contain TP rank and model info. - #1127 - [ ] (Multiple Replicas): Support a replica list for each KV item. - #1148 - [ ] (Layer-wise Storage): Maintaining layer info on the Mooncake side, supports stream transfer and communication overlap. ### Milestone 2: Master Service Enhancements This milestone enhances the Master component to support new storage architectures and routing logic. - [ ] (Key-based Routing): Implement new key-based routing capabilities in the Master service. - [ ] (Metadata Adaptation - Storage): Adapt the Master's metadata management to support the new multi-level storage architecture. - [ ] (Recovery) kv metadata persistency https://github.com/kvcache-ai/Mooncake/pull/1451 - [ ] (KVCache Awareness Interface) Exposes hit ratio for different layers. - [ ] (Metadata Adaptation - HA): Upgrade metadata schema and logic to meet new High Availability (HA) requirements. - Snapshot - https://github.com/kvcache-ai/Mooncake/issues/1150 - https://github.com/kvcache-ai/Mooncake/pull/1381 - https://github.com/kvcache-ai/Mooncake/pull/1431 - https://github.com/kvcache-ai/Mooncake/pull/1465 - https://github.com/kvcache-ai/Mooncake/pull/1739 - Oplog - https://github.com/kvcache-ai/Mooncake/issues/1200 - https://github.com/kvcache-ai/Mooncake/pull/1451 - https://github.com/kvcache-ai/Mooncake/pull/1515 - Refactoring HA - https://github.com/kvcache-ai/Mooncake/issues/1648 - https://github.com/kvcache-ai/Mooncake/pull/1678 - https://github.com/kvcache-ai/Mooncake/pull/1722 - https://github.com/kvcache-ai/Mooncake/pull/1777 - [ ] (Multi-tenant): Support Multi-tenant with different models, users and auth keys ### Milestone 3: Worker: Multi-Level Storage Architecture This is a major epic to build the next-generation multi-level storage system within the Worker. - 3.1: Abstraction & Caching - [ ] (Storage Abstraction Layer): Design and implement the core abstraction layer for multi-level storage. - [ ] (Cache Scheduling Interface): Design the abstract interface for cache scheduling logic. - [x] (Eviction Logic): Implement basic data eviction logic within the new storage architecture. - @Vincent-Bo-ali #1028 - [ ] (LRU Cache): Implement an LRU (Least Recently Used) policy as the default cache scheduling strategy. - [ ] (Local Client Cache): Keep a local cache for better performance. - #1062 - 3.2: Storage Backend Implementation - [ ] (DRAM Adaptation): Adapt the storage layer for DRAM, including support for NUMA affinity. - [ ] (SSD Adaptation): Adapt the storage layer for SSDs, enabling local external storage read/write capabilities. - #968 - #1031 - #1054 - #1203 - #1284 - #1319 - #1456 - [ ] (VRAM Adaptation): Adapt the storage layer to utilize VRAM. - [ ] (Huawei NPU Adaptation): Implement support for Huawei NPUs (H2D). - @ascend-direct-dev - 3.3: Elastic KVCache Storage - [ ] (KVCache Migration): Move KVCache between Mooncake client. - #1326 - [ ] (Data Replica): Dynamic Replication - #1100 ### Milestone 4: Worker: Networking & Elasticity This milestone focuses on refactoring worker communication and enabling resource elasticity. - [ ] (RPC Refactor): [Phase 1] Refactor the worker's read/write logic to replace RDMA with RPC-based communication. - [x] (Barex Transport Support): Support Alibaba barex transport in TE for Mooncake Store. - #1045 - [ ] (Resource Elasticity): Implement single-worker resource elasticity. - [ ] (Event‑driven completion): Provide an option to using event-driven notification worker instead of busy-polling. - #1033 - #1053 - [ ] (IPv6 Support): Support IPv6 in client, master and metadata server. - #1043 - #1067 ### Milestone 5: Deployment & Operations This milestone covers K8s integration (i.e., RBG, https://github.com/sgl-project/rbg) and build process improvements. - [ ] (K8s Autoscaling): Implement support for Kubernetes-based autoscaling of worker and dummy client instances. - [ ] (Scenario-based Builds): Implement a build system capable of producing different worker binaries optimized for different scenarios. - [ ] (Integration With AI Configurator): Use AI Configurator for better measuring Resource workers and other configurations. - [ ] (Deployment Documentation & Guides): Create comprehensive, up-to-date deployment documentation and step-by-step setup guides to simplify installation and configuration for all environments. ### Milestone 6: Conductor This feature implements a standalone or co-located conductor for use by the Gateway. @zhongzhouTan-coder @yejj710 @Liziqi-77 @Keithwwa @Asher-XunZhang - [x] (Basic design) : Discuss and design the standard specifications for KV indexer and kv-events - https://github.com/kvcache-ai/Mooncake/issues/1403 - https://github.com/kvcache-ai/Mooncake/issues/1527 - [ ] (KV indexer): Implement a Mooncake KV-Store Indexer service (include /quey, /query_by_hash ..etc) - [ ] (KV publisher): support kv-event publisher in Mooncake store - [ ] (Service registration): support two methods for service registration: either through static configuration files or dynamic service registration APIs - [ ] (KV events): Support consuming KV events generated by inference frameworks and Mooncake ### Milestone 7: Hardware Compatibility - [ ] (AMD) HIP, ROCm, MORI - #1154 - #1179 - #1189 - [ ] (Moore Thread) - #973 - #1176 - [ ] (AWS EFA) - #1509 - #1523 - #1526 ### Milestone 8: Pytorch Eco-system - [ ] (Support Tensor Attributions) - [ ] (Native torch format offload) ### Milestone 9: CI & CD enhancement - [ ] (End-to-end CI tests): For SGLang, support Hicache, PD, Elatics EP, checkpoint engine tests. - SGLang PD E2E CI: #1089 - Mooncake EP CI: #1099 - SGLang HiCache E2E CI: #1181 - vLLM 1P1D E2E CI: #1491 ### Milestone 10: Performance & Benchmarks - [ ] (Store Master Benchmark): Design and integrate a dedicated benchmark for the Mooncake store master module to evaluate throughput, latency, and scalability. #1201 - [ ] (SSD/NVMe storage benchmark) : #1388 ### Milestone 11: RL Infra - [ ] (ROLL): - [ ] (Slime/Miles) : - https://github.com/THUDM/slime/pull/1709 - https://github.com/radixark/miles/pull/591 ----- Thanks for being a part of the Mooncake community! Welcome to ****discuss**** and contribute! ----- **If you have any ideas, just leave a comment below and help shape the Roadmap.**

bsakaguchi added the

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2026-10-29 21:13:00 +00:00

bsakaguchi commented

2026-10-29 21:13:01 +00:00

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@Keithwwa commented on GitHub (Nov 11, 2025):

Celebrate! Finally, we have the V3 Roadmap!
I have a few questions I'd like to ask:

What is the motivation of 'key-based routing'?
Have you considered more diversified cache scheduling strategies, or designed scalability for it?
In addition, could you share some details about the 'Cache Scheduling Interface' and its design concepts?

@Keithwwa commented on GitHub (Nov 11, 2025): Celebrate! Finally, we have the V3 Roadmap! I have a few questions I'd like to ask: 1. What is the motivation of 'key-based routing'? 2. Have you considered more diversified cache scheduling strategies, or designed scalability for it? 3. In addition, could you share some details about the 'Cache Scheduling Interface' and its design concepts?

bsakaguchi commented

2026-10-29 21:13:01 +00:00

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@stmatengss commented on GitHub (Nov 11, 2025):

Celebrate! Finally, we have the V3 Roadmap! I have a few questions I'd like to ask:

What is the motivation of 'key-based routing'?

Have you considered more diversified cache scheduling strategies, or designed scalability for it?

In addition, could you share some details about the 'Cache Scheduling Interface' and its design concepts?

It means we can provide KVCache location query service for Router.
No updates now; you can give any suggestions, and I will add them to the Roadmap.
It includes migration, promotion/demotion, and hot-data detection (hot-data will have more replicas).

@stmatengss commented on GitHub (Nov 11, 2025): > Celebrate! Finally, we have the V3 Roadmap! I have a few questions I'd like to ask: > > 1. What is the motivation of 'key-based routing'? > 2. Have you considered more diversified cache scheduling strategies, or designed scalability for it? > 3. In addition, could you share some details about the 'Cache Scheduling Interface' and its design concepts? 1. It means we can provide KVCache location query service for Router. 2. No updates now; you can give any suggestions, and I will add them to the Roadmap. 3. It includes migration, promotion/demotion, and hot-data detection (hot-data will have more replicas).

bsakaguchi commented

2026-10-29 21:13:01 +00:00

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@zhongzhouTan-coder commented on GitHub (Nov 14, 2025):

Congratulations, just a big v3 roadmap!!!!
I am very interesting in the Cache Scheduling such as hot data migration, I hope I can join the contribution and do some work to help build a more powerful project~ ╰(°▽°)╯

@zhongzhouTan-coder commented on GitHub (Nov 14, 2025): Congratulations, just a big v3 roadmap!!!! I am very interesting in the Cache Scheduling such as hot data migration, I hope I can join the contribution and do some work to help build a more powerful project~ ╰(*°▽°*)╯

bsakaguchi commented

2026-10-29 21:13:01 +00:00

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@stmatengss commented on GitHub (Nov 14, 2025):

Congratulations, just a big v3 roadmap!!!! I am very interesting in the Cache Scheduling such as hot data migration, I hope I can join the contribution and do some work to help build a more powerful project~ ╰(°▽°)╯

Cool! It's a important feature for elastic deployment. Welcom to join the slack channel for offline discussion.

@stmatengss commented on GitHub (Nov 14, 2025): > Congratulations, just a big v3 roadmap!!!! I am very interesting in the Cache Scheduling such as hot data migration, I hope I can join the contribution and do some work to help build a more powerful project~ ╰(_°▽°_)╯ Cool! It's a important feature for elastic deployment. Welcom to join the slack channel for offline discussion.

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@stmatengss commented on GitHub (Jan 27, 2026):

Mooncake Omni & Disaggregation

flowchart TB
    subgraph Client ["Client / Request"]
        Req["Multimodal Request"]
    end

    subgraph Inference_Arch ["Upper-Level Inference Architecture (EPD)"]
        direction TB
        Router["Router / Scheduler"]
        
        subgraph Compute_Nodes ["Compute Nodes"]
            Encoder["Encoder Nodes<br/>(Multimodal Processing)"]
            Prefill["Prefill Nodes<br/>(Context Processing)"]
            Decode["Decode Nodes<br/>(Token Generation)"]
        end
        
        Tokenizer["Tokenizer +<br/>MM Pre-processor"]
    end

    subgraph Mooncake_System ["Mooncake Disaggregated System"]
        direction TB
        MC_Store[("Mooncake Store /<br/>Transfer Engine")]
        
        %% Logical Data Stores within Mooncake
        subgraph Data_Types ["Data Managed"]
            KV_Store[("1. KV Cache Storage")]
            Emb_Store[("4. Embedding Data Storage")]
            Rollout_Store[("2. RL Rollout Data Storage")]
            Weight_Store[("3. Model Weights Storage<br/>(Checkpoint Engine)")]
        end
    end

    subgraph RL_Train_Frame ["RL & Training Framework"]
        direction TB
        Rollout_Worker["Rollout Workers<br/>(Inference Actors)"]
        Trainer["Training Workers<br/>(Learners)"]
    end

    %% Inference Flow
    Req --> Tokenizer
    Tokenizer --> Router
    Router --> Encoder
    
    Encoder -- "Compute Embeddings" --> Emb_Store
    Encoder -.-> Prefill
    
    Emb_Store -- "Zero-Copy Transfer" --> Prefill
    Prefill -- "Generate & Store KV" --> KV_Store
    Prefill -- "Handover" --> Decode
    
    KV_Store <-- "Load/Offload KV" --> Decode

    %% RL & Training Flow
    Rollout_Worker -- "Generate Experience" --> Rollout_Store
    Rollout_Store -- "Fetch Batch" --> Trainer
    
    Trainer -- "Update Weights" --> Weight_Store
    Weight_Store -- "Distribute New Weights" --> Rollout_Worker
    Weight_Store -. "Update Serving Weights" .-> Compute_Nodes

    %% Styles
    style Mooncake_System fill:#f9f,stroke:#333,stroke-width:2px,color:black
    style Data_Types fill:#fff,stroke:#666,stroke-dasharray: 5 5,color:black
    style KV_Store fill:#ffdb99,stroke:#d48806
    style Emb_Store fill:#ffdb99,stroke:#d48806
    style Rollout_Store fill:#b5f5ec,stroke:#08979c
    style Weight_Store fill:#d9f7be,stroke:#389e0d

@stmatengss commented on GitHub (Jan 27, 2026): Mooncake Omni & Disaggregation ```mermaid flowchart TB subgraph Client ["Client / Request"] Req["Multimodal Request"] end subgraph Inference_Arch ["Upper-Level Inference Architecture (EPD)"] direction TB Router["Router / Scheduler"] subgraph Compute_Nodes ["Compute Nodes"] Encoder["Encoder Nodes (Multimodal Processing)"] Prefill["Prefill Nodes (Context Processing)"] Decode["Decode Nodes (Token Generation)"] end Tokenizer["Tokenizer + MM Pre-processor"] end subgraph Mooncake_System ["Mooncake Disaggregated System"] direction TB MC_Store[("Mooncake Store / Transfer Engine")] %% Logical Data Stores within Mooncake subgraph Data_Types ["Data Managed"] KV_Store[("1. KV Cache Storage")] Emb_Store[("4. Embedding Data Storage")] Rollout_Store[("2. RL Rollout Data Storage")] Weight_Store[("3. Model Weights Storage (Checkpoint Engine)")] end end subgraph RL_Train_Frame ["RL & Training Framework"] direction TB Rollout_Worker["Rollout Workers (Inference Actors)"] Trainer["Training Workers (Learners)"] end %% Inference Flow Req --> Tokenizer Tokenizer --> Router Router --> Encoder Encoder -- "Compute Embeddings" --> Emb_Store Encoder -.-> Prefill Emb_Store -- "Zero-Copy Transfer" --> Prefill Prefill -- "Generate & Store KV" --> KV_Store Prefill -- "Handover" --> Decode KV_Store <-- "Load/Offload KV" --> Decode %% RL & Training Flow Rollout_Worker -- "Generate Experience" --> Rollout_Store Rollout_Store -- "Fetch Batch" --> Trainer Trainer -- "Update Weights" --> Weight_Store Weight_Store -- "Distribute New Weights" --> Rollout_Worker Weight_Store -. "Update Serving Weights" .-> Compute_Nodes %% Styles style Mooncake_System fill:#f9f,stroke:#333,stroke-width:2px,color:black style Data_Types fill:#fff,stroke:#666,stroke-dasharray: 5 5,color:black style KV_Store fill:#ffdb99,stroke:#d48806 style Emb_Store fill:#ffdb99,stroke:#d48806 style Rollout_Store fill:#b5f5ec,stroke:#08979c style Weight_Store fill:#d9f7be,stroke:#389e0d ```

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@koussayinsat12 commented on GitHub (Feb 14, 2026):

Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible.

@koussayinsat12 commented on GitHub (Feb 14, 2026): Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible.

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@stmatengss commented on GitHub (Feb 15, 2026):

Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible.

Welcome to join Mooncake Community. Let's collaborate on more features

@stmatengss commented on GitHub (Feb 15, 2026): > Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible. Welcome to join Mooncake Community. Let's collaborate on more features

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@koussayinsat12 commented on GitHub (Feb 15, 2026):

I am new to contribution. Is there like first good issue. I can start with ?

Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible.

Welcome to join Mooncake Community. Let's collaborate on more features

@koussayinsat12 commented on GitHub (Feb 15, 2026): I am new to contribution. Is there like first good issue. I can start with ? > > Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible. > > Welcome to join Mooncake Community. Let's collaborate on more features

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@stmatengss commented on GitHub (Feb 16, 2026):

I am new to contribution. Is there like first good issue. I can start with ?

Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible.

Welcome to join Mooncake Community. Let's collaborate on more features

You can join the slack, and please check this list (https://github.com/kvcache-ai/Mooncake/issues/414).

@stmatengss commented on GitHub (Feb 16, 2026): > I am new to contribution. Is there like first good issue. I can start with ? > > > > Hi, guys congratulations on the new roadmap. I am also interested in joining you as contributor to learn and to try to advance what is possible. > > > > > > Welcome to join Mooncake Community. Let's collaborate on more features You can join the slack, and please check this list (https://github.com/kvcache-ai/Mooncake/issues/414).

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@Dayuxiaoshui commented on GitHub (Apr 1, 2026):

@stmatengss Hello, I officially implemented Metax for Mooncake two weeks ago. I'd also like to contribute to the Milestone 7: Hardware Compatibility project. Thank you.It's pullrequest https://github.com/kvcache-ai/Mooncake/pull/1731

@Dayuxiaoshui commented on GitHub (Apr 1, 2026): @stmatengss Hello, I officially implemented Metax for Mooncake two weeks ago. I'd also like to contribute to the Milestone 7: Hardware Compatibility project. Thank you.It's pullrequest https://github.com/kvcache-ai/Mooncake/pull/1731

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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@stmatengss commented on GitHub (Apr 2, 2026):

@stmatengss Hello, I officially implemented Metax for Mooncake two weeks ago. I'd also like to contribute to the Milestone 7: Hardware Compatibility project. Thank you.It's pullrequest #1731

Thanks for contributing! PRs and issues are welcome. Let me know if you have any questions.

@stmatengss commented on GitHub (Apr 2, 2026): > [@stmatengss](https://github.com/stmatengss) Hello, I officially implemented Metax for Mooncake two weeks ago. I'd also like to contribute to the Milestone 7: Hardware Compatibility project. Thank you.It's pullrequest [#1731](https://github.com/kvcache-ai/Mooncake/pull/1731) Thanks for contributing! PRs and issues are welcome. Let me know if you have any questions.

bsakaguchi commented

2026-10-29 21:13:02 +00:00

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[GH-ISSUE #1035] [RoadMap][Call For Contributions] Mooncake Store V3 Roadmap #342

Milestone 1: Core Architecture Refactor & Decoupling

Milestone 2: Master Service Enhancements

Milestone 3: Worker: Multi-Level Storage Architecture

Milestone 4: Worker: Networking & Elasticity

Milestone 5: Deployment & Operations

Milestone 6: Conductor

Milestone 7: Hardware Compatibility

Milestone 8: Pytorch Eco-system

Milestone 9: CI & CD enhancement

Milestone 10: Performance & Benchmarks

Milestone 11: RL Infra