tenstorrent/tt-metal
Dec 2024 – Sep 2025
8 Months active
Languages Used
C++PythonYAML
Technical Skills
GPU programmingPerformance optimizationTensor operationsUnit testingDeep LearningMachine Learning
Iva Potkonjak
PROFILE
Iva Potkonjak
Ivan Potkonjak developed and optimized advanced deep learning features for the tenstorrent/tt-metal repository, focusing on Stable Diffusion XL (SDXL) model infrastructure, performance, and reliability. He engineered scalable sharded transformer and VAE pipelines, introduced program caching, and enhanced group normalization and memory management to improve inference speed and model quality. Ivan’s work included extensive unit testing, CI/CD integration, and debugging to ensure robust releases and rapid iteration. Leveraging C++, Python, and PyTorch, he delivered modular, test-driven solutions that enabled efficient model deployment and experimentation. His contributions demonstrated technical depth in model optimization, parallel computing, and end-to-end workflow validation.
Overall Statistics
Feature vs Bugs
66%Features
Repository Contributions
161Total
Bugs
37
Commits
161
Features
73
Lines of code
37,825
Activity Months8
Your Network
742 people
Work History
September 2025
30 Commits • 15 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for tenstorrent/tt-metal. Delivered extensive SDXL feature work with broadened test coverage, significant model scalability enhancements, and robust reliability improvements. The month focused on validating SDXL components, scaling sharded training/inference pipelines, optimizing the VAE/encoder stack, and tightening release hygiene. Key accomplishments reflect a strong balance of business value and technical excellence: expanded test coverage to validate DRAM and refiner group norms, enabled scalable SDXL architectures with sharded transformers and complete 1280 sharding, improved run-time performance with a program cache and post-rebase optimizations, and strengthened the VAE/encoder stack with multiple updates while maintaining release reliability through CI fixes and code hygiene.
30 Commits • 15 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for tenstorrent/tt-metal. Delivered extensive SDXL feature work with broadened test coverage, significant model scalability enhancements, and robust reliability improvements. The month focused on validating SDXL components, scaling sharded training/inference pipelines, optimizing the VAE/encoder stack, and tightening release hygiene. Key accomplishments reflect a strong balance of business value and technical excellence: expanded test coverage to validate DRAM and refiner group norms, enabled scalable SDXL architectures with sharded transformers and complete 1280 sharding, improved run-time performance with a program cache and post-rebase optimizations, and strengthened the VAE/encoder stack with multiple updates while maintaining release reliability through CI fixes and code hygiene.
September 2025
August 2025
10 Commits • 3 Features
Aug 1, 2025Concise monthly summary for August 2025 focusing on SDXL-related work in tenstorrent/tt-metal. The work prioritized stability, performance, and efficiency to accelerate time-to-value for inference and experimentation, while improving model quality through enhanced text processing.
August 2025
10 Commits • 3 Features
Aug 1, 2025Concise monthly summary for August 2025 focusing on SDXL-related work in tenstorrent/tt-metal. The work prioritized stability, performance, and efficiency to accelerate time-to-value for inference and experimentation, while improving model quality through enhanced text processing.
July 2025
32 Commits • 17 Features
Jul 1, 2025July 2025 monthly summary for tenstorrent/tt-metal. Delivered extensive SDXL enhancements and broadened test coverage, strengthening reliability and developer velocity. Key features delivered include UNet and VAE unit tests, SDXL binary generation set for UNet, SDXL tiled VAE, and SDXL VAE optimization. Reinstated SDXL device performance tests and added didt tests; improved CI reliability through reorganization and fixes, and introduced weights caching to reduce load times. Also addressed post-rebase issues and aligned with updated SDXL architecture by removing unnecessary VAE usage where applicable. Overall, these efforts increased test coverage, enabled faster feedback loops, and improved stability for releases.
32 Commits • 17 Features
Jul 1, 2025July 2025 monthly summary for tenstorrent/tt-metal. Delivered extensive SDXL enhancements and broadened test coverage, strengthening reliability and developer velocity. Key features delivered include UNet and VAE unit tests, SDXL binary generation set for UNet, SDXL tiled VAE, and SDXL VAE optimization. Reinstated SDXL device performance tests and added didt tests; improved CI reliability through reorganization and fixes, and introduced weights caching to reduce load times. Also addressed post-rebase issues and aligned with updated SDXL architecture by removing unnecessary VAE usage where applicable. Overall, these efforts increased test coverage, enabled faster feedback loops, and improved stability for releases.
July 2025
June 2025
46 Commits • 26 Features
Jun 1, 2025June 2025 performance summary for tenstorrent/tt-metal: Focused SDXL delivery and reliability improvements enhancing model quality, performance, and stability across the TT-Metal stack. Delivered several SDXL-focused features and optimizations, stabilized CI/test pipelines, and expanded testing coverage. Key outcomes include faster SDXL inference, improved memory management, and stronger integration with PCC and VAE predictive coding, driving business value in model quality, reliability, and developer velocity.
June 2025
46 Commits • 26 Features
Jun 1, 2025June 2025 performance summary for tenstorrent/tt-metal: Focused SDXL delivery and reliability improvements enhancing model quality, performance, and stability across the TT-Metal stack. Delivered several SDXL-focused features and optimizations, stabilized CI/test pipelines, and expanded testing coverage. Key outcomes include faster SDXL inference, improved memory management, and stronger integration with PCC and VAE predictive coding, driving business value in model quality, reliability, and developer velocity.
May 2025
17 Commits • 5 Features
May 1, 2025May 2025 monthly summary for tenstorrent/tt-metal: Focused on SDXL VAE quality, memory/configuration optimizations, and demonstration performance improvements. Delivered core VAE enhancements and flexible IO/tensor shape handling, along with model and UNet optimizations to improve generation speed, stability, and resource utilization. Implemented a new SDXL demo scheduler with program cache tuning and added a optional VAE group normalization fallback to improve testing flexibility and accuracy. Addressed critical VAE conv issues and reinforced test stability, ultimately delivering higher-quality outputs with better CI reliability and broader hardware compatibility.
17 Commits • 5 Features
May 1, 2025May 2025 monthly summary for tenstorrent/tt-metal: Focused on SDXL VAE quality, memory/configuration optimizations, and demonstration performance improvements. Delivered core VAE enhancements and flexible IO/tensor shape handling, along with model and UNet optimizations to improve generation speed, stability, and resource utilization. Implemented a new SDXL demo scheduler with program cache tuning and added a optional VAE group normalization fallback to improve testing flexibility and accuracy. Addressed critical VAE conv issues and reinforced test stability, ultimately delivering higher-quality outputs with better CI reliability and broader hardware compatibility.
May 2025
April 2025
22 Commits • 4 Features
Apr 1, 2025April 2025 TT-Metal monthly summary focused on accelerating SDXL readiness and strengthening core module infrastructure. Key features landed include Pybind bindings and initial construction of a new SDXL operation to enable Python-level integration, scaffolding of SDXL base modules and interfaces, and batch-2 module adaptations to prepare for scaling, plus a new capability to download float32 weights. Core SDXL progress also delivered Functional UNet blocks to support SDXL architectures. In parallel, we addressed stability and CI gaps with multiple targeted fixes across tests and runtime behavior to reduce integration risk and ensure reliable CI signals. Overall impact: This work established the foundation for SDXL-enabled workflows, improved codebase modularity and test coverage, and reduced time-to-value for SDXL features. It positions the project to iterate quickly on SDXL support, including model loading, weight handling, and runtime optimizations, while stabilizing the development pipeline. Technologies/skills demonstrated: Pybind11/C++–Python interoperability, SDXL architectural design, modular base interfaces, batch processing patterns, test-driven validation, CI reliability engineering, and weight management tooling.
April 2025
22 Commits • 4 Features
Apr 1, 2025April 2025 TT-Metal monthly summary focused on accelerating SDXL readiness and strengthening core module infrastructure. Key features landed include Pybind bindings and initial construction of a new SDXL operation to enable Python-level integration, scaffolding of SDXL base modules and interfaces, and batch-2 module adaptations to prepare for scaling, plus a new capability to download float32 weights. Core SDXL progress also delivered Functional UNet blocks to support SDXL architectures. In parallel, we addressed stability and CI gaps with multiple targeted fixes across tests and runtime behavior to reduce integration risk and ensure reliable CI signals. Overall impact: This work established the foundation for SDXL-enabled workflows, improved codebase modularity and test coverage, and reduced time-to-value for SDXL features. It positions the project to iterate quickly on SDXL support, including model loading, weight handling, and runtime optimizations, while stabilizing the development pipeline. Technologies/skills demonstrated: Pybind11/C++–Python interoperability, SDXL architectural design, modular base interfaces, batch processing patterns, test-driven validation, CI reliability engineering, and weight management tooling.
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for tenstorrent/tt-metal: Key features delivered include hybrid parallelism for Llama3 models using submeshes, enabling independent execution across device subsets while preserving tensor parallelism, and SDXL model testing coverage enhancements with unit tests for ttnn.conv2d and ttnn.group_norm to validate configurations, input shapes, and performance across diverse scenarios. No major bugs fixed this month. Overall impact: improved deployment flexibility, scalability, and reliability; enhanced validation reduces risk in SDXL deployments. Technologies/skills demonstrated: submesh-based hybrid parallelism, tensor parallelism strategies, unit testing, SDXL/ttnn validation, and CI/test framework proficiency.
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for tenstorrent/tt-metal: Key features delivered include hybrid parallelism for Llama3 models using submeshes, enabling independent execution across device subsets while preserving tensor parallelism, and SDXL model testing coverage enhancements with unit tests for ttnn.conv2d and ttnn.group_norm to validate configurations, input shapes, and performance across diverse scenarios. No major bugs fixed this month. Overall impact: improved deployment flexibility, scalability, and reliability; enhanced validation reduces risk in SDXL deployments. Technologies/skills demonstrated: submesh-based hybrid parallelism, tensor parallelism strategies, unit testing, SDXL/ttnn validation, and CI/test framework proficiency.
March 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 Monthly Summary: Focused on delivering a targeted performance optimization for tensor kernels in the tt-metal repository. The changes adjust read and compute kernels to optimize height reduction across varying DRAM read patterns and improve bank utilization during tensor processing. This work is tracked in commit 0a73de03aff5190c22dec7e9b4eb27c3da161097 (related to #15498 and #15501). Overall impact includes improved tensor operation throughput and memory efficiency, enabling faster, more predictable performance for workloads that are memory-bound. No major bug fixes documented this month; the emphasis was on deliverables, code quality, and preparation for benchmarking and release readiness.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 Monthly Summary: Focused on delivering a targeted performance optimization for tensor kernels in the tt-metal repository. The changes adjust read and compute kernels to optimize height reduction across varying DRAM read patterns and improve bank utilization during tensor processing. This work is tracked in commit 0a73de03aff5190c22dec7e9b4eb27c3da161097 (related to #15498 and #15501). Overall impact includes improved tensor operation throughput and memory efficiency, enabling faster, more predictable performance for workloads that are memory-bound. No major bug fixes documented this month; the emphasis was on deliverables, code quality, and preparation for benchmarking and release readiness.
Activity
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Quality Metrics
Correctness90.6%
Maintainability82.8%
Architecture85.4%
Performance86.4%
AI Usage43.4%
Skills & Technologies
Programming Languages
C++PythonYAML
Technical Skills
AI DevelopmentAI developmentAI model deploymentC++C++ DevelopmentC++ developmentC++ programmingCI/CDCUDA programmingContinuous IntegrationConvolutional Neural NetworksData MovementData ProcessingDeep LearningDevOps
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