llm-d/llm-d
May 2026 – Jul 2026
3 Months active
Languages Used
MarkdownYAML
Technical Skills
configuration managementdocumentationContainerizationDevOpsKubernetesCloud Infrastructure
Amit Berman
PROFILE
Amit Berman
Worked on the llm-d/llm-d repository over three months, focusing on deployment reliability and resource alignment for large language model infrastructure. Addressed critical configuration issues by correcting ConfigMap references and fixing container deployment for OpenShift, ensuring smoother onboarding and reducing support overhead. Implemented an engine-type labeling system for SGLang to optimize core metrics extraction and aligned CPU and memory resource limits with vLLM standards. Enhanced cache management by adding emptyDir volumes for /.cache and /.triton, mirroring vLLM’s approach. Utilized Kubernetes, YAML, and containerization best practices, with careful attention to documentation and configuration management to improve reproducibility and stability.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
31
Activity Months3
Your Network
1210 peopleSame Organization
@ibm.com1079
Arun VMember
Aayush-PanchalMember
Aayush KumarMember
Abdallah AbdelaalMember
Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026: Delivered SGLang Engine-Type Labeling for Core Metrics and vLLM Resource Alignment in llm-d/llm-d. Implemented engine-type: sglang label to optimize metrics extraction, aligned CPU/memory resources with vLLM, and added matching /.cache and /.triton emptyDir volumes to mirror vLLM caches. The work also addresses core-metrics extraction failures when engine-type is unset by aligning metric naming with vLLM and baseline patches for stable deployment.
1 Commits • 1 Features
Jul 1, 2026July 2026: Delivered SGLang Engine-Type Labeling for Core Metrics and vLLM Resource Alignment in llm-d/llm-d. Implemented engine-type: sglang label to optimize metrics extraction, aligned CPU/memory resources with vLLM, and added matching /.cache and /.triton emptyDir volumes to mirror vLLM caches. The work also addresses core-metrics extraction failures when engine-type is unset by aligning metric naming with vLLM and baseline patches for stable deployment.
July 2026
June 2026
1 Commits
Jun 1, 2026June 2026: Fixed OpenShift tokenizer deployment in llm-d/llm-d and aligned guide to improve reliability and reproducibility. The fix addresses container configuration, updates the tokenizer installation path, and adjusts model server replicas to match the documented guidance. Result: smoother deployments and reduced support overhead.
June 2026
1 Commits
Jun 1, 2026June 2026: Fixed OpenShift tokenizer deployment in llm-d/llm-d and aligned guide to improve reliability and reproducibility. The fix addresses container configuration, updates the tokenizer installation path, and adjusts model server replicas to match the documented guidance. Result: smoother deployments and reduced support overhead.
May 2026
1 Commits
May 1, 2026Month 2026-05: Fixed a critical configuration issue in llm-d/llm-d to ensure workload processing uses the correct ConfigMap, improving reliability and deployment stability.
1 Commits
May 1, 2026Month 2026-05: Fixed a critical configuration issue in llm-d/llm-d to ensure workload processing uses the correct ConfigMap, improving reliability and deployment stability.
May 2026
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
MarkdownYAML
Technical Skills
Cloud InfrastructureContainerizationDevOpsKubernetesconfiguration managementdocumentation
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