mlcommons / mlcommons/endpoints

[Roadmap] Inference Endpoints Phase 2 Roadmap

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type: RFC
Dominant language
Python
Stars
21
Forks
28
Avg merge
3d 17h
Merged PRs (30d)
13

Description

We will document the issues that are required to reach key milestones in this document.

How to join us

  • Subscribe to our mailing list: link
  • Join our Slack: link

Goal

Post-GTC / Q2 2026: Stabilize the benchmarking platform, expand accuracy coverage, harden reliability, and close all known P0 gaps following the GTC demo.

See Phase 1 roadmap: #83


ShowStopper

  • Accuracy evaluation for LLMs (GPQA, AIME, HLE, etc.) #4
  • Throughput/latency parity with MLPerf LoadGen and vLLM benchmark_serving #8
  • Pareto submission clarification (models, datasets, points) #84

Functionality (P0)

  • Submission ruleset interaction with config (Phase 4 integration) #5
  • Define and implement the final report structure #6
  • Analyze roofline of inference endpoints (offline/online/SSE) #9
  • Pub/Sub implementation for event recorder #183
  • Remove unsafe serialization support (security: arbitrary code execution risk) #184
  • EVAL and SUBMISSION test types raise bare NotImplementedError — add user-friendly errors #218

Bug Fixes

  • PostgreSQL DuplicatePreparedStatement error in recorder #213
  • max_throughput mode causes connection timeouts #202
  • Performance discrepancy between submission config and performance-only config #199
  • max_duration_ms ends online test prematurely before all samples are issued #197
  • target_qps hardcoded to 10.0 in Offline mode instead of None #219
  • RuntimeSettings stores Random objects — benchmarks non-reproducible and non-serializable #221
  • Errors seen running endpoint tests in local mode #99
  • llama.cpp incompatibility (requires OpenAI-compatible server) #133

Accuracy & Datasets (P1)

  • MMLU / MMLU-Pro — dataset + scorer + extractor #176
  • MATH500 — dataset integration #177
  • Healthbench integration #178
  • Humanity's Last Exam (HLE) integration #179

Performance & Benchmarking (P1)

  • System bottleneck tests (client stress tests) #10
  • Explain text vs token performance regression on TRTLLM #182
  • Implement BURST and STEP load patterns #217

Testing & CI (P0/P1)

  • Add GitLab CI to stress test upper bound of endpoint measurement #138
  • SGLang adapter integration tests are permanently skipped #220
  • KVStore, ServiceLauncher, and MetricsAggregator services lack integration tests #222

UI/UX & Visualization (P1)

  • Runtime visualization of the benchmark #7

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the roadmap checklist and the linked issues for the milestone areas that still need work. This issue is complete when the required Phase 2 work is tracked and the roadmap accurately reflects the status of its linked issues.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
documentation
Issue type
Documentation
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
25/100

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