Development Roadmap (2026 Q1)

[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

• 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

• 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:

  • [Feature] Add DCP support for GQA with flashinfer #14982
  • [feature] implement dcp for deepseek_v2 #14194
  • [Feature] Add DCP support for DeepSeek v3.2 #18167
  • [Feature] Helix (TP + CP) Parallelism: A2A Communication + Decoupled Attention/FFN Parallelism #19436

• 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

• 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

• 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)

• 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

• 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:
  • 25Q3: [Roadmap] Diffusion (2025 Q4) #12799
  • 26Q1: [Roadmap] SGLang-Diffusion (26 Q1) #18286
• Slack: #diffusion

Multimodal Models

• Day-0 support for major models; add more OCR models
  Contributors: @mick @JustinTong0323 @yuan-luo

• Performance improvements: better prefix & embedding cache

• 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

• 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 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)

• PoC: Zehuan Li, Jinwei Yao, Chenyang Zhao

• RFC: Block Diffusion Large Language Model (dLLM) Framework

• Roadmap: [Roadmap] Diffusion LLMs (2025 Q4 & 2026 Q1) #14199

Hardware

• AMD roadmap (2025 Q4): @HaiShaw
  • [Roadmap] AMD Instinct Development (2025Q4) #12890
• TPU roadmap (2025 Q4)
  • Development Roadmap (2025 Q4) sglang-jax#190
  • Slack: #dev-jax-tpu
• NPU roadmap (2025 Q4): @iforgetmyname @ZhengdQin
  • [Roadmap] Ascend NPU Development (2026 Q1) #13664
• Intel CPU/XPU roadmap (2025 Q4):
  • [Roadmap] Intel CPU Roadmap (2025Q4) #12802
  • [Roadmap] Intel XPU Roadmap (2025Q4) #12806
• Better multi-backend abstraction: @Alcanderian

Model Coverage

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

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

• 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

• CI suites refactor: [Roadmap] CI suites organization #13808

  • PoC: @alisonshao @Kangyan-Zhou

• 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

• Improve nightly tests

  • Add more models (Deepseek, GPT-OSS, Qwen3-next)

• Full feature coverage CI with all combinations (every two days)

• 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

[m8ngotree]

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

[ping1jing2]

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

[weimin023]

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]

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

[mengniwang95]

@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]

@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