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Development Roadmap (2026 Q1) #12780
Description
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
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Turn on overlap scheduler for speculative decoding by default
PoC: @hnyls2002
Slack: #spec-decoding
Issue: [Feature] Overlap Spec Support #11762 -
Turn on prefill CUDA graph by default
PoC: @Oasis-Git @ispobock @BBuf
Slack: #piecewise-cuda-graph
Issue: [Feature] Roadmap for Prefill (Piecewise) CUDA Graph #11490 -
General memory pool and prefix cache for hybrid models
PoC: @cctry @xiezhq-hermann
Slack: #prefix-cache, #kv-cache-store
Issue: [Feature] Memory Cache System Refactoring Road Map (Mem Cache V2) #12587 -
Mixed chunked prefill refactor
PoC: @hzh0425 @yizhang2077
Issue: [Feature] Mixed ChunkPrefill Optimization Roadmap #13626 -
Torch compile stack (Looking for PoC)
Slack: #torch-compile
PR: [WIP] Support torch compile based pass manager framework #10987
Issue: [RFC] SGLang unified kernel fusion and torch compile optimisations #10118 -
SRT core/plugin refactor
Goal: make the core reusable, so people can do customization easily and maintain their out-of-the-tree code. -
DP attention and attention backend refactor
Goal: make attention backends fully stateless, unify the sync positions of dp attention.
Parallelism
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Pipeline parallelism refactor for long-context prefill and high-throughput decoding
PoC: @ShangmingCai
Slack: #pipeline-parallel
Issue: [Roadmap] Pipeline parallelism roadmap #11857 -
Expert parallelism refactor
PoC: @ch-wan
Slack: #expert-parallel
Issue: [Roadmap] MoE Refactor #8715
Elastic parallel PRs: [1/N] Introduce Mooncake Backend and Mooncake EP to Support Elastic EP #10423, [4/N]Elastic EP support deepep backend #11837 -
Context parallelism
Prefill CP: [Feature] Support context parallel for Qwen3 model #16632
Megatron SP: [WIP][Feature] support tp-sp on qwen2/3 & deepseek v2/3/3.2 #12820
Decode CP: -
Compatibility goals
- All parallelisms + speculative decoding
- All parallelisms + PD disaggregation
- Multiple load balancing strategies for DP attention/system (minimal tokens, shortest queue) [Feature] Load Balance Refactor for DP-Attention #16080
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GB200/GB300 NVL72 optimizations
PoC: @Fridge003 @fzyzcjy
More details in PD Disaggregation/Large Scale Serving section of SGLang Nvidia Collaboration Roadmap (2026 Q1) #17130
Slack: #deepseek-large-scale-serving
Server Reliability
- Illegal memory access fixes. [Bug] illegal memory access / illegal instruction / memory leak #11968
- Runtime memory/paging checker.
- Grammar crash fault tolerance.
- Server crash fault tolerance.
Kernel
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JIT kernels
Roadmap: [Roadmap] JIT kernel development #17035 [Feature] sgl-kernel wheel slimming plan tracking #17865
PoC: @DarkSharpness -
Integrate Flashinfer kernels
More details in Flashinfer section of SGLang Nvidia Collaboration Roadmap (2026 Q1) #17130
Slack: #flashinfer-kernels -
Tune FP8 gemm in Cutlass
Slack: #kernel-dev -
Communication kernel work
Slack: #kernel-dev- NCCL symmetric memory (PRs: Add support for NCCL symmetric memory for TP allreduces #8238, Register allgather/reducescatter buffers with symm memory #12572)
- Overlap TP communication with compute (e.g., Support TP overlap #9058)
- Integrate additional A2A kernels (e.g., pplx)
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Automated nightly fusion detection
Workflow: https://github.com/sgl-project/sglang/actions/runs/19004823026
Slack: #ci-cd-build-release
Speculative Decoding
- General speculative algorithm abstraction to support multiple algorithms
- Hybrid algorithm combining Eagle and ngram
- Adaptive algorithm that adjusts speculative parameters during runtime
- Support for dllm draft models in sglang, associated with SpecForge RFC: dLLM (DFlash) Online Training in SpecForge SpecForge#412 @jinleic @yilian49 @xiaomin-D @sleepcoo
- Slack: #spec-decoding
PD Disaggregation
- Support radix cache on decode engines [P/D disagg] - support decode side radix cache #19746
- Refactor scheduler loop to reuse more code
- More plans: [Roadmap] Distributed Serving Enhancement on 2025 H2 #8210
- Auto scaling in OME
- Comprehensive NIXL and Dynamo integration
- Slack: #pd-disaggregation
KV Cache System & Memory Pool
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PoC: @xiezhq-hermann
Issue: [Feature] HiCache for Hybrid and Sparse LLMs #12826.
slack #kv-cache-store -
Sparse attention and KV cache scheduler for GPU/CPU
PR: [Feature] Support Sparse Attention and KV cache scheduling between CPU and GPU for GQA/DSA. #11191
Diffusion (Multimodal Generation)
- PoC: @mickqian
- Roadmap:
- Slack: #diffusion
Multimodal Models
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Day-0 support for major models; add more OCR models
Contributors: @mick @JustinTong0323 @yuan-luo -
Performance improvements: better prefix & embedding cache
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Faster CUDA IPC in MQ for large video/images
PR: [FEAT] Shared mem pool based cuda ipc for multi-modal data transport #11917 -
Omni Support [RFC] SGLang-Omni Design #16546
Slack: #multi-modal
Quantization
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General support for various quantization formats and refactor
Issue: [Roadmap] Quantization Modifications #15194 -
ModelOpt support
PoC: @Edwardf0t1
More details in Model Optimizer section of SGLang Nvidia Collaboration Roadmap (2026 Q1) #17130
Slack: #modelopt -
Communication quantization (fp4/fp8 allreduce/allgather/alltoall)
Slack: #quantization
Multi-LoRA Serving
- Major roadmap: [Roadmap] Lora Support #2929
PoC: @Fridge003 - LoRA for speculative decoding Support spec decoding when LoRA is applied to target model #12903
Contributor: @ConnorLi96 @lifuhuang - Overlap Weight Loading with Compute [Feature] overlap LoRA weight loading with compute #15512
Contributor: @glenliu21 @ConnorLi96 @lifuhuang - LoRA for MoE layers [LoRA][III] Add LoRA support for MoE layers and enable TP #14105
Contributors: @ConnorLi96 @Jonahcb
Slack: #lora
Prefill-Only
- Major roadmap: [Roadmap] SGLang Prefill-Only 2026 CY26H1 and CY26H2 Design Proposal & Roadmap #15344
PoC: @sundar24295s
Slack: #prefill-only
RL Framework Integration
- AReaL, slime, verl integration (sorted alphabetically)
- Customized weight refitting from RDMA, etc @zhaochenyang20 @JD-ETH
- Open recipe of large-scale MoE training (Deepseek/Kimi/GLM) + GRPO training
- Systematic and algorithm mitigation for training-inference mismatch @zhaochenyang20 @fzyzcjy @Fridge003 @zyzshishui
- Support SGLang Gateway as the DP scheduler for rollout in the RL framework
- Tinker-like serverless RL APIs; @zhaochenyang20
- Native NVFP8 Training; @GeLee-Q @xieck13 @fy1214
- VLM RL with FSDP; @nanjiangwill @minleminzui
- Speculative Training; @guapisolo
Slack: #reinforcement-learning, #slime-rl-framework
Diffusion Language Models (DLLMs)
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PoC: Zehuan Li, Jinwei Yao, Chenyang Zhao
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Roadmap: [Roadmap] Diffusion LLMs (2025 Q4 & 2026 Q1) #14199
Hardware
- AMD roadmap (2025 Q4): @HaiShaw
- TPU roadmap (2025 Q4)
- NPU roadmap (2025 Q4): @iforgetmyname @ZhengdQin
- Intel CPU/XPU roadmap (2025 Q4):
- Better multi-backend abstraction: @Alcanderian
Model Coverage
- Day-0 model support for all major models
PoC: @wisclmy0611 @JustinTong0323
Slack: #dev
Model Gateway & API Layer
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Support multimodality and image processor in gRPC mode
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Support PII and classify API for classifying intent and complexity of the input
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Semantic Routing Support
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Allow Gateway to actively listen to SGLang server's KV cache events to better handle routing decisions in gRPC mode
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Allow SGLang server to start with both gRPC and HTTP server
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Model Gateway terminal UI
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Reactive UI to launch workers remotely; this should support both local machines and remote
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Natively support Anthropic Message API instead of wrapping around chat completion in gRPC mode
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Gateway SDK, supporting GoLang, Python, and Node.js for every Rust crate (policies, tokenizer, parsers, etc)
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Metrics enhancement, including tracing, model-specific metrics (TTFT, TPOT, etc)
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PoC: @slin1237 @CatherineSue
Issue: SGLang Autonomous Model Gateway Roadmap #13098
Slack: #router-sig
Tracing and Profiling
- Roadmap of request tracing: HiCache, PP, and SD. [Roadmap] roadmap of request tracing (2025 Q4 and 2026 Q1) #13511
Advanced Priority Scheduling
- [Roadmap] Priority based traffic management (priority scheduling, batching, and concurrency control) #13526
PoC: @harrisonlimh
CI / Release / Maintenance
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CI suites refactor: [Roadmap] CI suites organization #13808
- PoC: @alisonshao @Kangyan-Zhou
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Improve CI monitor workflow
- Automatically track accuracy & performance metrics with standard format
- Regression detection & alerts
- Add performance dashboard for popular model (deepseek r1) in track of performance changes
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Improve nightly tests
- Add more models (Deepseek, GPT-OSS, Qwen3-next)
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Full feature coverage CI with all combinations (every two days)
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Coverage of latest hardware (B300/GB200)
More details in CI/CD section of SGLang Nvidia Collaboration Roadmap (2026 Q1) #17130
Slack: #ci-cd-build-release, #help-desk
Activity
[-][Feature][/-][+]Development Roadmap (2025 Q4)[/+][-]Development Roadmap (2025 Q4)[/-][+]Development Roadmap (2026 Q1)[/+]m8ngotree commentedon Jan 29, 2026
Hi, I am interested in and currently working on communication quantization. I am currently working on a PR for quantized AllReduce.
ping1jing2 commentedon Jan 30, 2026
Hi @m8ngotree , would you mind leaving your comments on #15194
weimin023 commentedon Jan 30, 2026
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?
Kangyan-Zhou commentedon Jan 30, 2026
@weimin023 You can probably ask in #kernel-dev slack channel and go over active issues to see whether you find anything interested
mengniwang95 commentedon Feb 2, 2026
@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?
hshen14 commentedon Feb 2, 2026
CC @mingfeima
savvadesogle commentedon Feb 3, 2026
Hello @mengniwang95
could you please add support for the A770 as well? (DG2, Alchemist)
mengniwang95 commentedon Feb 5, 2026
Hi @savvadesogle , our quantization method generally is device agnostic, we validate it on B60
kamathhrishi commentedon Mar 1, 2026
@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?
xingsy97 commentedon Mar 3, 2026
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.
[-]Development Roadmap (2026 Q1)[/-][+]Development Roadmap (2026 Q2)[/+][-]Development Roadmap (2026 Q2)[/-][+]Development Roadmap (2026 Q1)[/+]huangyibo commentedon Apr 30, 2026
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?
JD-ETH commentedon May 1, 2026
@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?
txh1873749380 commentedon May 6, 2026
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
github-actions commentedon Jul 6, 2026
This issue has been automatically closed due to inactivity. Please feel free to reopen it if needed.