Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 21:54 UTC

intel / ipex-llm

Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.

PythonApache-2.0★ 8,864 stars⑂ 1,428 forkssince Aug 2016archivedView on GitHub ↗
KindCommand-line toolNetwork servicehow this is determined

intel/ipex-llm holds a health index of 19 out of 100, placing it in the Critical band. It scores highest on Community & Adoption (95/100) and lowest on Vitality (30/100). The repository is archived, so no further maintenance is expected.

19
overall / 100
Critical

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

19
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

5,484 followers1,351 public repossince Mar 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIipex_llmpoints to another repo — not scored2.2.0-578492 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

30At Risk · 21% of overall
How it's scored
3.6/36Push recencylast push 196 days ago
3.5/36Commit cadence5/52 weeks with commits
7.6/18Commit volume6 commits in the last year
0/10OpenSSF Scorecard: Maintainedproject is archived
Inputs used
commits_last_year6
human_commit_share1
days_since_last_push196
active_weeks_last_year5
How it's scored
27/27Ships releases22 releases published
7.2/36Release recencylatest release 483 days ago
12.6/27Release cadencea release every ~161.8 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count22
latest_release_tagv2.3.0-nightly
releases_from_tagsno
days_since_latest_release483
mean_days_between_releases161.8
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

95Exceptional · 17% of overall
How it's scored
60/60Stars8,864 stars
25/25Forks1,428 forks
13.5/15Watchers264 watchers
Inputs used
forks1,428
stars8,864
watchers264
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

91Excellent · 23% of overall
How it's scored
54/54Bus factor11 contributor(s) cover half of all commits
20/22.5Commit distributiontop contributor authored 11% of commits
13.5/13.5Contributor breadth100 contributors
10/10OpenSSF Scorecard: Contributorsproject has 10 contributing companies or organizations
Inputs used
bus_factor11
contributors_sampled100
top_contributor_share0.11
How it's scored
25.2/42Issue resolution60% of issues closed
26.9/30PR acceptance8,965/10,014 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
13.5/15OpenSSF Scorecard: Code-ReviewFound 28/30 approved changesets -- score normalized to 9
Inputs used
merged_prs8,965
open_issues1,213
closed_issues1,811
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.599
closed_unmerged_prs1,049
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
25/25Owner reach5,484 followers of intel
25/25Track record1,351 public repos, account ~10 yr old
Inputs used
followers5,484
owner_typeOrganization
is_verified
owner_loginintel
public_repos1,351
account_age_days3,801
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

64Moderate · 19% of overall
How it's scored
24/24CI workflows9 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
2/20OpenSSF Scorecard: CI-Tests3 out of 29 merged PRs checked by a CI test -- score normalized to 1
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics4 topics
10/10Wiki
Inputs used
topicspytorch, llm, transformers, gpu
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

50Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0.8/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
0.2/2.5CI-Tests3 out of 29 merged PRs checked by a CI test -- score normalized to 1
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-ReviewFound 28/30 approved changesets -- score normalized to 9
2.5/2.5Contributorsproject has 10 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintainedproject is archived
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
3.5/5SASTSAST tool detected but not run on all commits
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities36 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

39Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history97 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.97
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environmentDockerfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
53.9/55Manageable file sizes14/670 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes261,577
source_files_sampled670
oversized_source_files14
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examplesexample
Inputs used
example_dirsexample
has_mcp_signalno
api_schema_files
interfaces_expected_ofnetwork-service

Key facts

8,864GitHub stars
100contributors
6commits, last 12 months
196days since last push
22releases
11bus factor
1,213open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'ipex_llm' points at a different repository (https://github.com/intel-analytics/ipex-llm); excluded from ecosystem scoring

More detail

Star and fork history 0 ★ / 1,428 ⇿
0Stars
1,428Forks
19Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

05001,0001,5001,428292017-122022-042026-08
Major 1Minor 12Patch 3

Each point covers 8 days.

OpenSSF Scorecard 5.0 / 10
5.0aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-12 21:54 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
1CI-Tests3 out of 29 merged PRs checked by a CI test -- score normalized to 1
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 28/30 approved changesets -- score normalized to 9
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject is archived
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
7SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
0Vulnerabilities36 existing vulnerabilities detected
All dependencies 0

Full resolved dependency set from the GitHub dependency graph: 0 direct and 0 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies to assess

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.