tenstorrent/tt-inference-server
Oct 2025 – May 2026
7 Months active
Languages Used
PythonShellJSON
Technical Skills
CLI DevelopmentContinuous IntegrationData EvaluationDevOpsMachine LearningPython Development
Adam Roberge
PROFILE
Adam Roberge
Worked on the tenstorrent/tt-inference-server repository, delivering robust evaluation and benchmarking systems for GenAI and multimodal inference workloads. Developed features such as a CLI for evaluation filtering, automated plugin installation, and expanded device benchmarking, focusing on reliability and reproducibility. Integrated vision token calculations for variable-length models and implemented crash fixes and OOM remediations to stabilize batch and hardware-specific inference. Enhanced reporting with detailed percentile metrics and improved trace capture for multimodal tasks. Leveraged Python, JSON, and shell scripting, emphasizing CI/CD, error handling, and data validation to streamline developer workflows and ensure production-ready, scalable model evaluation infrastructure.
Overall Statistics
Feature vs Bugs
85%Features
Repository Contributions
25Total
Bugs
2
Commits
25
Features
11
Lines of code
5,738
Activity Months7
Your Network
427 peopleWork History
May 2026
4 Commits • 3 Features
May 1, 2026May 2026 — tt-inference-server: Focused on delivering targeted evaluation tooling, startup robustness, and broader hardware benchmarking. Highlights include a new Evaluation Filtering CLI, automated vllm-tt-plugin installation for local servers, and expanded P300X2/P150 device support with updated benchmarks. Also addressed reliability issues through defensive input handling, safer test mocks, and lint improvements, reducing CI noise and improving developer experience.
4 Commits • 3 Features
May 1, 2026May 2026 — tt-inference-server: Focused on delivering targeted evaluation tooling, startup robustness, and broader hardware benchmarking. Highlights include a new Evaluation Filtering CLI, automated vllm-tt-plugin installation for local servers, and expanded P300X2/P150 device support with updated benchmarks. Also addressed reliability issues through defensive input handling, safer test mocks, and lint improvements, reducing CI noise and improving developer experience.
May 2026
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-inference-server. Focused on stabilizing batch inference workloads and expanding evaluation capabilities through multimodal support. Delivered a critical OOM remediation for batched prefill in the inference engine and integrated multimodal support for large-small Mistral models, along with targeted resource-management tuning to improve concurrency, stability, and throughput. These changes position the project to handle higher-throughput workloads and richer evaluation scenarios with predictable performance in production environments.
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-inference-server. Focused on stabilizing batch inference workloads and expanding evaluation capabilities through multimodal support. Delivered a critical OOM remediation for batched prefill in the inference engine and integrated multimodal support for large-small Mistral models, along with targeted resource-management tuning to improve concurrency, stability, and throughput. These changes position the project to handle higher-throughput workloads and richer evaluation scenarios with predictable performance in production environments.
February 2026
1 Commits
Feb 1, 2026February 2026 focused on stabilizing TT inference workloads on TT hardware. Implemented a targeted crash fix by disabling torch.compile for TT hardware, addressing a crash path and improving reliability for users on TT systems. The change was implemented in tenstorrent/tt-inference-server with commit 12a40439ec9ca149338d68dd52b7a63186549a5e. This work reduces crash-induced downtime, lowers support burden, and strengthens production readiness across TT deployments.
1 Commits
Feb 1, 2026February 2026 focused on stabilizing TT inference workloads on TT hardware. Implemented a targeted crash fix by disabling torch.compile for TT hardware, addressing a crash path and improving reliability for users on TT systems. The change was implemented in tenstorrent/tt-inference-server with commit 12a40439ec9ca149338d68dd52b7a63186549a5e. This work reduces crash-induced downtime, lowers support burden, and strengthens production readiness across TT deployments.
February 2026
January 2026
9 Commits • 2 Features
Jan 1, 20262026-01 Monthly Summary: Delivered a robust benchmarking ecosystem for AIPerf/GenAI-Perf and improved reliability for multimodal inference tracing, enabling clearer, data-driven performance decisions for GenAI workloads. Key features delivered: - Benchmarking framework and reporting enhancements for AIPerf/GenAI-Perf with separate text and image benchmark reports; introduced VLM vs image distinctions and a new token-throughput metric; added percentile tables and improved CLI/data formatting; regression fix for benchmarks data retrieval. - GenAI-Perf VLM/Image benchmarking support with dedicated report structures, detailed percentile sections (mean, P50, P99) for TTFT/TPOT/E2EL, and unified output directories; added --device/--model CLI arguments for consistency. - Inference server trace capture reliability improvements: text-only traces are captured first, with multimodal traces following when image resolutions are specified. - Stability and coverage improvements for benchmark reporting: restored support and correct display for embedding, audio, CNN, and non-VLM models; improved task_type handling and display dictionaries; addressed lint/formatting issues to align with CI expectations. Major bugs fixed: - Regression fix for benchmarks data retrieval and multiple report generation issues across embedding, image generation, audio, CNN; corrected VLM vs image separation and display logic; ensured backward-compatible report formats and CI lint compliance. Overall impact and accomplishments: - Significantly improved benchmarking fidelity, reliability, and interpretability across text, image, and VL/M benchmarks, enabling faster, data-driven optimization of GenAI inference workloads. - Reduced reporting defects and improved developer experience through cleaner CLI, robust reports, and CI-friendly formatting. Technologies/skills demonstrated: - AIPerf/GenAI-Perf benchmarking, VLM vs image differentiation, detailed percentile reporting; display_dict design for report composition. - Python tooling, JSON parsing, and robust report generation; CLI enhancements with device/model parameters. - Code quality and CI readiness via ruff formatting and linting, regression fixes, and comprehensive testable fixes. - Tracing instrumentation and reliability improvements for multimodal inference.
January 2026
9 Commits • 2 Features
Jan 1, 20262026-01 Monthly Summary: Delivered a robust benchmarking ecosystem for AIPerf/GenAI-Perf and improved reliability for multimodal inference tracing, enabling clearer, data-driven performance decisions for GenAI workloads. Key features delivered: - Benchmarking framework and reporting enhancements for AIPerf/GenAI-Perf with separate text and image benchmark reports; introduced VLM vs image distinctions and a new token-throughput metric; added percentile tables and improved CLI/data formatting; regression fix for benchmarks data retrieval. - GenAI-Perf VLM/Image benchmarking support with dedicated report structures, detailed percentile sections (mean, P50, P99) for TTFT/TPOT/E2EL, and unified output directories; added --device/--model CLI arguments for consistency. - Inference server trace capture reliability improvements: text-only traces are captured first, with multimodal traces following when image resolutions are specified. - Stability and coverage improvements for benchmark reporting: restored support and correct display for embedding, audio, CNN, and non-VLM models; improved task_type handling and display dictionaries; addressed lint/formatting issues to align with CI expectations. Major bugs fixed: - Regression fix for benchmarks data retrieval and multiple report generation issues across embedding, image generation, audio, CNN; corrected VLM vs image separation and display logic; ensured backward-compatible report formats and CI lint compliance. Overall impact and accomplishments: - Significantly improved benchmarking fidelity, reliability, and interpretability across text, image, and VL/M benchmarks, enabling faster, data-driven optimization of GenAI inference workloads. - Reduced reporting defects and improved developer experience through cleaner CLI, robust reports, and CI-friendly formatting. Technologies/skills demonstrated: - AIPerf/GenAI-Perf benchmarking, VLM vs image differentiation, detailed percentile reporting; display_dict design for report composition. - Python tooling, JSON parsing, and robust report generation; CLI enhancements with device/model parameters. - Code quality and CI readiness via ruff formatting and linting, regression fixes, and comprehensive testable fixes. - Tracing instrumentation and reliability improvements for multimodal inference.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Vision Token Calculation for Variable-Length Models (VLMs) in tenstorrent/tt-inference-server, enabling token-based max concurrency calculations for image workloads and more reliable benchmarking. Integrated vision tokens into the concurrency benchmarking logic, resulting in accurate concurrency estimates, better benchmarking metrics, and improved resource planning. As part of this change, 16k isl for text+image benchmarks was removed to optimize benchmarks. This work enhances throughput forecasting and reliability for image-driven inference tasks.
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Vision Token Calculation for Variable-Length Models (VLMs) in tenstorrent/tt-inference-server, enabling token-based max concurrency calculations for image workloads and more reliable benchmarking. Integrated vision tokens into the concurrency benchmarking logic, resulting in accurate concurrency estimates, better benchmarking metrics, and improved resource planning. As part of this change, 16k isl for text+image benchmarks was removed to optimize benchmarks. This work enhances throughput forecasting and reliability for image-driven inference tasks.
December 2025
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for tenstorrent/tt-inference-server focused on evaluation pipeline improvements and tooling alignment.
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for tenstorrent/tt-inference-server focused on evaluation pipeline improvements and tooling alignment.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025: Delivered substantial enhancements to the tt-inference-server evaluation and deployment workflows. Implemented evaluation framework improvements for Qwen3-8B and Gemma 3 models, and overhauled Hugging Face integration to simplify setup and usage. These changes increase benchmarking reliability, accelerate model iteration, and reduce operational friction for the team.
3 Commits • 2 Features
Oct 1, 2025October 2025: Delivered substantial enhancements to the tt-inference-server evaluation and deployment workflows. Implemented evaluation framework improvements for Qwen3-8B and Gemma 3 models, and overhauled Hugging Face integration to simplify setup and usage. These changes increase benchmarking reliability, accelerate model iteration, and reduce operational friction for the team.
October 2025
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Quality Metrics
Correctness93.6%
Maintainability84.0%
Architecture86.4%
Performance84.0%
AI Usage47.2%
Skills & Technologies
Programming Languages
JSONPythonShell
Technical Skills
AI IntegrationAI model integrationBenchmarkingCI/CDCLI DevelopmentContinuous IntegrationData EvaluationData ValidationDependency managementDevOpsDevice ConfigurationError HandlingJSON handlingMachine LearningModel Deployment
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