SGLang Roadmap — 2026 Q2

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, hierarchical cache, 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, image and 3D generation. Omni model support.

Basic Feature Refactors and Improvements

• Scheduler refactor
  PoC: @hnyls2002
  Slack: #dev, #spec-decoding
  Goals: Make forward-mode more general; Make the scheduler more stateless; Fully support mixed chunked prefill; Hide CPU overhead in scheduler for all cases; Move more preparation into cuda graph. [Feature] Overlap Spec Support #11762

• KV Cache management
  PoC: @ispobock @hzh0425 @xiezhq-hermann
  Slack: #kv-cache-store, #hybrid-model
  Goals: Make hierarchical cache and hybrid attention the native feature; Support flexible session control for agentic workloads

  • [Roadmap]: SGLang Distributed KVCache System For Agentic Workload #21846
  • [Roadmap] Unified Hybrid Radix Cache Refactor #20415
  • Integration with elasticmem #13581

• Speculative decoding
  PoC: @Qiaolin-Yu
  Slack: #spec-decoding
  Goals: General abstraction for more spec algorithms; General abstraction for spec graph preparation & init; Adaptive spec configurations for different requests and batch sizes.

  • Speculative Decoding Development Roadmap (2026 Q2) #23005

• PD disaggregation
  PoC: @ShangmingCai
  Slack: #pd-disaggregation
  Goals: [Roadmap] Prefill-Decode Disaggregation Roadmap (2026 Q2) #21703

• API Server
  PoC: @alexnails
  Slack: #sglang-grpc-rfc-22558 #rust-migration
  Goals: [RFC] Native gRPC Server for SGLang in Rust #22558

• Rust migration
  PoC: @ishandhanani @rainj-me
  Slack: #rust-migration
  Goals: Gradually rewrite most components (scheduler, api server, prefix tree) in Rust

• Cuda graph runner backend
  PoC: @Oasis-Git
  Slack: #piecewise-cuda-graph
  Goals: Support flexible cuda graph backends (decode, prefill) x (full, breakable, torch-compile-based pcg); Enable breakable cuda graph for prefill by default. [RFC] Cuda Graph Runner Backend Refactor #23004

Parallelism

• Pipeline parallelism refactor for long-context prefill and high-throughput decoding
  PoC: @ShangmingCai
  Slack: #pipeline-parallel
  Issue: [Roadmap] Pipeline parallelism roadmap #11857

• Expert parallelism
  Slack: #expert-parallel
  Issue: [Roadmap] GB200/GB300 development for Q2 #19650, [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

• Data parallelism attention refactor
  Issue: [Feature] Load Balance Refactor for DP-Attention #16080

• Context parallelism
  PoC: @Fridge003 @kpham-sgl @ShangmingCai @ch-wan
  Slack: #context-parallel
  Issue: [Roadmap] Context Parallelism (2026 Q2) #21788

• Distributed Weight Data Parallelism
  PoC: @yuhao318
  Issue: [Feature] Distributed Weight Data Parallelism (DWDP) for Sparse MoE Models #22084

• GB200/GB300 NVL72 optimizations
  PoC: @Fridge003
  Slack: #deepseek-large-scale-serving
  Issue: [Roadmap] GB200/GB300 development for Q2 #19650

Multimodal

• Diffusion and Multimodal Generation
  Slack: #diffusion
  Issue: [Roadmap] SGLang-Diffusion (26 Q2) #23035

• MLLM, VLM, and Multimodal Perception
  Slack: #multi-modal
  Issue: [Roadmap] Multimodal LLM (26 Q2) #23036

• SGLang Omni
  PoC: @zhaochenyang20 @FrankLeeeee
  Repo: https://github.com/sgl-project/sglang-omni
  Currently supported: Fish Audio S2 Pro (Dual AR), Qwen3-Omni (Thinker-Talker).
  Q2 goals:

  • RFC #188 refactor: collapse Stage→Worker→Executor→Engine to Stage→Engine; ~33K → ~10K lines; request-path depth 8–10 → 6 with no accuracy/perf regression.
  • Day-zero serving for new Omni/audio-gen models: 3+ models at production quality; integration cost ~2 weeks → <1 week post-refactor.
  • Benchmark CI: extend the task × model framework (PR add cert functionality #223) with audio quality metrics, Video MMU, calibrated regression thresholds.
  • Production observability: per-stage latency breakdown, token-level tracing, audio quality monitoring.
  • Performance: generalize S2 Pro's CUDA Graph + torch.compile path (55.8 → 120 TPS) into a reusable abstraction; close Qwen3 Omni Talker gap.
  • Omni RL: expose rollout interface to Miles (joint with RL workstream).

Hardware

• General multi-hardware abstraction
  PoC: @alexnails
  Issue: Multi platform Plugin #21388

• NVIDIA collaboration
  Issue: Nvidia Collaboration Roadmap (2026 Q2) #22960

• AMD extension & Specification on top of Q2 above
  PoC: @HaiShaw
  Issue: AMD Development Roadmap (2026 Q2) #23494

• TPU
  TorchTPU based solution
  Jax-based solution: Development Roadmap (2026 Q2) sglang-jax#909
  Slack: #dev-jax-tpu

• NPU
  PoC: @iforgetmyname
  Issue: NPU Development Roadmap (2026 Q2) #25598

• Intel CPU/XPU:

  • [Roadmap] Intel CPU Roadmap (2026Q2) #24921
  • [Roadmap] Intel XPU Roadmap (2026Q2) #24922

Kernels

• Experiment with MegaKenrel integration
• Move more kernels to JIT style
• Integrate more communication-compute overlap kernels
• Integrate more quantization kernels (nvfp4, mxfp8)

Reliability and Observability

• Dumping tools for fixing cuda illegal memory access
• Better per request tracing
• Runtime memory pool check, PD transfer checksum, weight checksum

RL Framework Integration

• Miles
  PoC: @yueming-yuan @fzyzcjy
  Repo: https://github.com/radixark/miles
  Landed: Unified FP8 E2E (blog); R3 routing replay for MoE (paper); INT4 QAT closed loop (blog); speculative RL with online SFT draft; zero-copy CUDA IPC weight sync; TIS/MIS off-policy correction; VLM multi-turn; MrlX multi-agent.
  Q2 goals: Zero mismatch for MoE RL; SGLang↔Megatron parity for MoE (TP/EP/PP); Diffusion / Omni / dLLM RL via shared rollout interface; elastic rollout-vs-training scheduling.

• slime, verl, AReaL
  PoC @zhaochenyang20
  slime, verl, AReaL — Maintain SGLang as a first-class rollout backend across the major external RL frameworks. (slime) is the upstream Miles tracks and the reference for SGLang-native recipes. (verl) is the industry-adopted Volcano Engine framework. (AReaL) is the async RL framework from Ant / Tsinghua.
  Goals: converge on one stable SGLang rollout-engine API to cut per-framework drift on weight sync, sampling, and logprob semantics; upstream shared primitives (R3, FP8, deterministic inference, TIS/MIS) so all four frameworks benefit together.

Multi-LoRA Serving

• More model support, improve perf and compatible with lora RL training in Miles
  PoC: @yushengsu-thu
  Issue: [Roadmap] Lora (2026 Q2) #25095

Model Coverage

• Day-0 model support for all major models
  PoC: @wisclmy0611 @JustinTong0323
  Slack: #dev

CI / Release / Maintenance

• Improve stability, increase coverage, and reduce flakiness.
  PoC: @alisonshao @Kangyan-Zhou
  Slack: #ci-cd-build-release
  Issue: [CI Infrastructure] Roadmap: Regression-Based CI Checks #21157, [Feature] Improve Unit Test Coverage #20865, [Tracking] SGLang CI/CD Test Coverage Improvements - Q2 2026 Roadmap #20847

• Mock model for correctness tests

TODO: This is still WIP. More sections will be added.