tenstorrent/tt-inference-server
Sep 2025 – Feb 2026
6 Months active
Languages Used
PythonMarkdownJSONDockerfileYAMLbashC++
Technical Skills
FastAPIasynchronous programmingaudio processingbackend developmentdata handlingdata processing
Zeljana Torlak
PROFILE
Zeljana Torlak
Zoran Torlak developed and maintained advanced inference and streaming features for the tenstorrent/tt-inference-server repository over six months, focusing on real-time audio transcription, multi-modal processing, and robust job orchestration. He implemented Whisper-based streaming endpoints and enhanced audio, image, and video processing pipelines using Python, FastAPI, and C++. Zoran improved code quality through refactoring, Ruff-based linting, and expanded unit testing, while optimizing Docker-based deployments and CI/CD workflows. His work included building a PR gate for LLM streaming performance, integrating concurrency controls, and refining API consistency, resulting in a scalable, maintainable backend that supports reliable, high-throughput AI inference services.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
84Total
Bugs
12
Commits
84
Features
38
Lines of code
21,111
Activity Months6
Your Network
379 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-inference-server: Key feature delivered is the PR Gate for Performance Testing of LLM Streaming against the C++ Server. This included infrastructure to build the C++ server, install necessary dependencies (Drogon, python3-dev), and run performance tests, with an automated gate that prevents merges until performance criteria are met. Commit 0bf91fa3d7cd666e15cb1fb501e2efff3f761fc6 documents and implements the gate and related test runner changes. Major bugs fixed include token counting fixes and log flushing improvements to stabilize measurements, as well as installation-step optimizations (removing unnecessary git steps). Overall impact: reduced risk of performance regressions in the critical LLM streaming path, faster, more reliable PR reviews for performance-sensitive changes, and improved observability through better logging and metrics. Technologies/skills demonstrated: C++ server setup, Drogon web framework, Python development tooling, performance testing, CI/CD gating, test automation, logging and metrics collection, and efficient dependency management.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-inference-server: Key feature delivered is the PR Gate for Performance Testing of LLM Streaming against the C++ Server. This included infrastructure to build the C++ server, install necessary dependencies (Drogon, python3-dev), and run performance tests, with an automated gate that prevents merges until performance criteria are met. Commit 0bf91fa3d7cd666e15cb1fb501e2efff3f761fc6 documents and implements the gate and related test runner changes. Major bugs fixed include token counting fixes and log flushing improvements to stabilize measurements, as well as installation-step optimizations (removing unnecessary git steps). Overall impact: reduced risk of performance regressions in the critical LLM streaming path, faster, more reliable PR reviews for performance-sensitive changes, and improved observability through better logging and metrics. Technologies/skills demonstrated: C++ server setup, Drogon web framework, Python development tooling, performance testing, CI/CD gating, test automation, logging and metrics collection, and efficient dependency management.
February 2026
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 (2026-01) highlights the ongoing evolution of the tt-inference-server platform with a focus on robust job orchestration, API consistency, and stability in containerized builds. The team delivered core lifecycle improvements, aligned APIs with development changes, and addressed reliability risks through targeted fixes and rollbacks where necessary.
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 (2026-01) highlights the ongoing evolution of the tt-inference-server platform with a focus on robust job orchestration, API consistency, and stability in containerized builds. The team delivered core lifecycle improvements, aligned APIs with development changes, and addressed reliability risks through targeted fixes and rollbacks where necessary.
December 2025
35 Commits • 15 Features
Dec 1, 2025December 2025 — Tenstorrent TT-Inference-Server Key features delivered: - Code quality and refactors: Ruff formatter configuration; rename decorators file; update DEVICE IDs naming to DEVICE_IDS_ALL. Commits: b931079324e55963d56e7cd1d805c380a66e1476; 6717acde1c7048ec907ab043500698899e98025f; 32de2cec3c72a2336f17312674ddc3f6e1f966d2. - Audio processing enhancements: added audio_chunk_duration_seconds calculation; support for additional Whisper params; removed language and task params from audio response. Commits: 6505c78db34ed042af39f2d371f6d12ffe9ad7f0; 287062c55970c4325903fb44d837dbf5094ebe79; b337ae0d88a3072a703866290668d915b9dd3287. - LLM streaming and worker lifecycle: introduced LLM streaming capability and stop_workers method for video/image services. Commits: 28ba94e0a7709a607ea469d7996c4c34635bd8b5; c7ad501c5d8ed6768790331802a5dabcbb8b4ca9. - Runtime dependencies and Docker/environment cleanup: install ffmpeg in runtime; cleanup environment variables in Dockerfiles (Dockerfile; Forge). Commits: 418133173531c5b9ee68ccbfdb3e7f934c6fdf94; 68a538cf1ed08bcc77bb9850f8deb72bf7caf26d; cedcd27ab32f73d01a88979bdbac24832f75744d. - Concurrency and tests maintenance: switch to asyncio.Queue for concurrency; fix unit test dependencies. Commits: 9d6a170473c41739af7d3641b84dc8e550e151f8; cab3aa5b5b1fa6f982c44a70c9fe2ec130125665. - Job Submission Subsystem: added support for job submission with concurrency locks and UT updates/docs. Commit: 6360856eff43eb8278659b295e3aecbb822a155a. - Deterministic seeding: replace generator param with seed for deterministic randomness. Commit: a73f45751873a0a2aa53f43e72a7b4f662019bc1. Major bugs fixed: - Apply fixes for issues #1358 and #1381. Commits: 81dc69431fab76cc5c1a848d0cb5f04cd01ce79b; 5a8c8ccb5ccc164adf4cb7286317123caa58e0bf. - Revert deleted lines to restore prior functionality. Commit: e84a60d329ec96364ea260236cd8b4717be28d2d. - Unit tests fixes to address failures from recent changes. Commit: 6adc65304684670c8c37a565fd996d05a59a989b. - Ruff format fix to resolve linter formatting issues. Commit: a094999296aa5010968d0fadb783523d34add6ee. - Bug fixes and polishing: miscellaneous fixes and cleanups (fix; remove job_db_path; polishing). Commits: 67326ff5d74c4fde438f8d7e811e5996958f647c; b4e3d10a2a1458bec90bf29db60d9ed4b6fa3c6d; d3ff16f727897aad94914cc4f4555af11f4be69d. Overall impact and accomplishments: - Significantly improved code quality, maintainability, and developer onboarding via linting, formatting, and documentation updates. - Enhanced inference capabilities with audio and Whisper parameter controls, deterministic seeding for reproducible experiments, and robust LLM streaming support. - Strengthened reliability and performance through concurrency improvements, unit test coverage, and CI improvements; reduced runtime environmental drift with FFmpeg support and Docker cleanup. - Enabled scalable job submission workflows with concurrency locks and better observability. Technologies and skills demonstrated: - Python tooling and static analysis (Ruff), typing, and clean coding practices. - Async programming (asyncio.Queue) and concurrency design. - Audio processing orchestration (Whisper params, duration calculations). - LLM streaming integration and worker lifecycle management. - Docker/FFmpeg-based runtime hardening and environment hygiene. - Testing, CI gating, and documentation practices for quality assurance and knowledge sharing.
35 Commits • 15 Features
Dec 1, 2025December 2025 — Tenstorrent TT-Inference-Server Key features delivered: - Code quality and refactors: Ruff formatter configuration; rename decorators file; update DEVICE IDs naming to DEVICE_IDS_ALL. Commits: b931079324e55963d56e7cd1d805c380a66e1476; 6717acde1c7048ec907ab043500698899e98025f; 32de2cec3c72a2336f17312674ddc3f6e1f966d2. - Audio processing enhancements: added audio_chunk_duration_seconds calculation; support for additional Whisper params; removed language and task params from audio response. Commits: 6505c78db34ed042af39f2d371f6d12ffe9ad7f0; 287062c55970c4325903fb44d837dbf5094ebe79; b337ae0d88a3072a703866290668d915b9dd3287. - LLM streaming and worker lifecycle: introduced LLM streaming capability and stop_workers method for video/image services. Commits: 28ba94e0a7709a607ea469d7996c4c34635bd8b5; c7ad501c5d8ed6768790331802a5dabcbb8b4ca9. - Runtime dependencies and Docker/environment cleanup: install ffmpeg in runtime; cleanup environment variables in Dockerfiles (Dockerfile; Forge). Commits: 418133173531c5b9ee68ccbfdb3e7f934c6fdf94; 68a538cf1ed08bcc77bb9850f8deb72bf7caf26d; cedcd27ab32f73d01a88979bdbac24832f75744d. - Concurrency and tests maintenance: switch to asyncio.Queue for concurrency; fix unit test dependencies. Commits: 9d6a170473c41739af7d3641b84dc8e550e151f8; cab3aa5b5b1fa6f982c44a70c9fe2ec130125665. - Job Submission Subsystem: added support for job submission with concurrency locks and UT updates/docs. Commit: 6360856eff43eb8278659b295e3aecbb822a155a. - Deterministic seeding: replace generator param with seed for deterministic randomness. Commit: a73f45751873a0a2aa53f43e72a7b4f662019bc1. Major bugs fixed: - Apply fixes for issues #1358 and #1381. Commits: 81dc69431fab76cc5c1a848d0cb5f04cd01ce79b; 5a8c8ccb5ccc164adf4cb7286317123caa58e0bf. - Revert deleted lines to restore prior functionality. Commit: e84a60d329ec96364ea260236cd8b4717be28d2d. - Unit tests fixes to address failures from recent changes. Commit: 6adc65304684670c8c37a565fd996d05a59a989b. - Ruff format fix to resolve linter formatting issues. Commit: a094999296aa5010968d0fadb783523d34add6ee. - Bug fixes and polishing: miscellaneous fixes and cleanups (fix; remove job_db_path; polishing). Commits: 67326ff5d74c4fde438f8d7e811e5996958f647c; b4e3d10a2a1458bec90bf29db60d9ed4b6fa3c6d; d3ff16f727897aad94914cc4f4555af11f4be69d. Overall impact and accomplishments: - Significantly improved code quality, maintainability, and developer onboarding via linting, formatting, and documentation updates. - Enhanced inference capabilities with audio and Whisper parameter controls, deterministic seeding for reproducible experiments, and robust LLM streaming support. - Strengthened reliability and performance through concurrency improvements, unit test coverage, and CI improvements; reduced runtime environmental drift with FFmpeg support and Docker cleanup. - Enabled scalable job submission workflows with concurrency locks and better observability. Technologies and skills demonstrated: - Python tooling and static analysis (Ruff), typing, and clean coding practices. - Async programming (asyncio.Queue) and concurrency design. - Audio processing orchestration (Whisper params, duration calculations). - LLM streaming integration and worker lifecycle management. - Docker/FFmpeg-based runtime hardening and environment hygiene. - Testing, CI gating, and documentation practices for quality assurance and knowledge sharing.
December 2025
November 2025
15 Commits • 4 Features
Nov 1, 2025November 2025 performance: Delivered multi-modal inference enhancements and governance improvements for tenstorrent/tt-inference-server. Introduced Whisper-based image and audio processing with new response formats and API enhancements; added video generation support in tt-media-server; standardized model naming and deprecated YOLOv4; and completed significant internal maintenance to boost reliability and developer productivity. Overall impact: broader product capabilities, reduced maintenance cost, and stronger platform scalability.
November 2025
15 Commits • 4 Features
Nov 1, 2025November 2025 performance: Delivered multi-modal inference enhancements and governance improvements for tenstorrent/tt-inference-server. Introduced Whisper-based image and audio processing with new response formats and API enhancements; added video generation support in tt-media-server; standardized model naming and deprecated YOLOv4; and completed significant internal maintenance to boost reliability and developer productivity. Overall impact: broader product capabilities, reduced maintenance cost, and stronger platform scalability.
October 2025
23 Commits • 13 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on delivering Whisper-based features, performance improvements, and maintainability enhancements for tenstorrent/tt-inference-server. The team expanded device support, improved audio handling, and strengthened observability to drive business value through higher accuracy, throughput, and reliability.
23 Commits • 13 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on delivering Whisper-based features, performance improvements, and maintainability enhancements for tenstorrent/tt-inference-server. The team expanded device support, improved audio handling, and strengthened observability to drive business value through higher accuracy, throughput, and reliability.
October 2025
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for tenstorrent/tt-inference-server: Real-time Whisper streaming transcription with speaker-aware chunk merging delivered. Implemented a real-time streaming endpoint and generalized streaming pipeline for live transcription with partial results; added speaker-aware VAD segment merging to handle speaker changes and duration constraints, improving transcription quality. This work establishes a robust streaming inference path, reduces latency, and improves multi-speaker transcription accuracy, enabling faster time-to-insight for end users.
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for tenstorrent/tt-inference-server: Real-time Whisper streaming transcription with speaker-aware chunk merging delivered. Implemented a real-time streaming endpoint and generalized streaming pipeline for live transcription with partial results; added speaker-aware VAD segment merging to handle speaker changes and duration constraints, improving transcription quality. This work establishes a robust streaming inference path, reduces latency, and improves multi-speaker transcription accuracy, enabling faster time-to-insight for end users.
Activity
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Quality Metrics
Correctness90.2%
Maintainability87.6%
Architecture87.8%
Performance88.4%
AI Usage31.4%
Skills & Technologies
Programming Languages
C++DockerfileJSONMarkdownPythonYAMLbash
Technical Skills
AI integrationAPI DevelopmentAPI developmentAPI integrationAsynchronous ProgrammingAudio ProcessingBackend DevelopmentC++ developmentCode FormattingCode RefactoringContainerizationContinuous IntegrationDevOpsDockerError Handling
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