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Development Roadmap (2026 Q1) #12780

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@hnyls2002

SGLang Roadmap — 2026 Q1

Contributions and feedback are welcome. Join Slack.

Focus

  • Feature compatibility & reliability: Full compatibility and production-level reliability across P/D disaggregation, all parallelisms, speculative decoding, HiCache, and load balancing.
  • Usability: Easy installation on NV/AMD/TPU/CPU; simple large-scale deployment (k8s, OME).
  • Kernel optimization for next-gen hardware (GB300/GB200, B300/B200, MI350/MI355, TPU).
  • Reinforcement learning framework integration and training-inference mismatch mitigation.
  • Multimodal: Enhance diffusion models for video and image generation. Omni model support.

Base Engine Features

Image

Parallelism

Server Reliability

Kernel

Speculative Decoding

PD Disaggregation

KV Cache System & Memory Pool

Diffusion (Multimodal Generation)

Multimodal Models

Quantization

Multi-LoRA Serving

Slack: #lora

Prefill-Only

Slack: #prefill-only

RL Framework Integration

Slack: #reinforcement-learning, #slime-rl-framework

Diffusion Language Models (DLLMs)

Hardware

Model Coverage

Model Gateway & API Layer

  • Support multimodality and image processor in gRPC mode

  • Support PII and classify API for classifying intent and complexity of the input

  • Semantic Routing Support

  • Allow Gateway to actively listen to SGLang server's KV cache events to better handle routing decisions in gRPC mode

  • Allow SGLang server to start with both gRPC and HTTP server

  • Model Gateway terminal UI

  • Reactive UI to launch workers remotely; this should support both local machines and remote

  • Natively support Anthropic Message API instead of wrapping around chat completion in gRPC mode

  • Gateway SDK, supporting GoLang, Python, and Node.js for every Rust crate (policies, tokenizer, parsers, etc)

  • Metrics enhancement, including tracing, model-specific metrics (TTFT, TPOT, etc)

  • PoC: @slin1237 @CatherineSue
    Issue: SGLang Autonomous Model Gateway Roadmap #13098
    Slack: #router-sig

Tracing and Profiling

Advanced Priority Scheduling

CI / Release / Maintenance

Slack: #ci-cd-build-release, #help-desk

Activity

  1. changed the title [-][Feature][/-] [+]Development Roadmap (2025 Q4)[/+] on Nov 6, 2025
  2. pinned this issue on Nov 6, 2025
  3. self-assigned this
    on Nov 6, 2025
  4. self-assigned this
    on Nov 6, 2025
  5. changed the title [-]Development Roadmap (2025 Q4)[/-] [+]Development Roadmap (2026 Q1)[/+] on Jan 12, 2026
  6. m8ngotree commented on Jan 29, 2026

    @m8ngotree

    Hi, I am interested in and currently working on communication quantization. I am currently working on a PR for quantized AllReduce.

  7. ping1jing2 commented on Jan 30, 2026

    @ping1jing2
    Collaborator

    Hi, I am interested in and currently working on communication quantization. I am currently working on a PR for quantized AllReduce.

    Hi @m8ngotree , would you mind leaving your comments on #15194

  8. weimin023 commented on Jan 30, 2026

    @weimin023
    Contributor

    Hi @ping1jing2, I have experience developing Triton and CUDA kernels for vLLM and would love to contribute to SGLang. Are there any specific performance bottlenecks or kernel-related tasks currently on the roadmap that I could help with?

  9. Kangyan-Zhou commented on Jan 30, 2026

    @Kangyan-Zhou
    Collaborator

    Hi @ping1jing2, I have experience developing Triton and CUDA kernels for vLLM and would love to contribute to SGLang. Are there any specific performance bottlenecks or kernel-related tasks currently on the roadmap that I could help with?

    @weimin023 You can probably ask in #kernel-dev slack channel and go over active issues to see whether you find anything interested

  10. mengniwang95 commented on Feb 2, 2026

    @mengniwang95
    Contributor

    @hnyls2002 Hi, here is the Intel neural compressor team from Intel. We plan to make some contributions to SGLang about quantization methods, could you please take a look at our roadmap and add it under Intel CPU/XPU roadmap section?

  11. hshen14 commented on Feb 2, 2026

    @hshen14

    @hnyls2002 Hi, here is the Intel neural compressor team from Intel. We plan to make some contributions to SGLang about quantization methods, could you please take a look at our roadmap and add it under Intel CPU/XPU roadmap section?

    CC @mingfeima

  12. savvadesogle commented on Feb 3, 2026

    @savvadesogle

    Intel CPU/XPU

    Hello @mengniwang95
    could you please add support for the A770 as well? (DG2, Alchemist)

    Image
  13. mengniwang95 commented on Feb 5, 2026

    @mengniwang95
    Contributor

    Intel CPU/XPU

    Hello @mengniwang95 could you please add support for the A770 as well? (DG2, Alchemist)

    Image

    Hi @savvadesogle , our quantization method generally is device agnostic, we validate it on B60

  14. kamathhrishi commented on Mar 1, 2026

    @kamathhrishi

    @hnyls2002 "Support PII and classify API for classifying intent and complexity of the input" is there a issue for this with more details? can I take it up?

  15. xingsy97 commented on Mar 3, 2026

    @xingsy97
    Contributor

    I opened #19709 to add native /classify API support, which covers part of "Support PII and classify API for classifying intent and complexity of the input". The PII API part is not included.

  16. changed the title [-]Development Roadmap (2026 Q1)[/-] [+]Development Roadmap (2026 Q2)[/+] on Apr 16, 2026
  17. changed the title [-]Development Roadmap (2026 Q2)[/-] [+]Development Roadmap (2026 Q1)[/+] on Apr 16, 2026
  18. unpinned this issue on Apr 16, 2026
  19. huangyibo commented on Apr 30, 2026

    @huangyibo

    Hi team, I’ve been working on RDMA and RPC systems for eight years, and I’m excited about the opportunity to contribute to such an innovative and active community! What would be a good place/task to start, e.g., RL infra over RDMA?

  20. JD-ETH commented on May 1, 2026

    @JD-ETH
    Contributor

    @huangyibo we just finished this https://www.lmsys.org/blog/2026-04-29-p2p-update/
    maybe think more on the hicache or PD related features?

  21. txh1873749380 commented on May 6, 2026

    @txh1873749380

    I checked the ds-v4-hopper branch: it only supports MoE routing via Marlin, but Marlin is limited to TP8 (single-node) and cannot span two machines. Even if we ignore the multi-node issue, pure Marlin sharding is indivisible for 16 GPUs—the expert/hidden dimensions don't divide evenly, so the partition mapping simply doesn't work. To solve multi-node scaling, DeepEP is required, yet DeepEP + Marlin is currently unsupported. This creates a hard deadlock: we can't run 16 GPUs in a single instance today. Will future releases support MixFP4 with a 16-GPU single-instance deployment? @Kangyan-Zhou

  22. github-actions commented on Jul 6, 2026

    @github-actions
    Contributor

    This issue has been automatically closed due to inactivity. Please feel free to reopen it if needed.

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