tenstorrent/tt-inference-server
Oct 2025 – Jun 2026
8 Months active
Languages Used
JSONPythonDockerfileMarkdownYAMLC++ShellBash
Technical Skills
API developmentBackend DevelopmentContainerizationDevOpsMachine LearningPython
Lana Jovanovic
PROFILE
Lana Jovanovic
Worked on the tenstorrent/tt-inference-server repository, delivering advanced AI model serving infrastructure with a focus on reliability, performance, and extensibility. Developed multiturn chat, streaming response pipelines, and OpenAI-compatible APIs, integrating new LLMs such as GPT-OSS and DeepSeek-V4-Pro. Enhanced backend systems using Python and C++, implementing dynamic configuration, session caching, and benchmarking frameworks. Improved deployment workflows through Docker-based containerization and CI/CD automation, while optimizing concurrency and resource management. Addressed stability and performance by refining tokenization, caching, and protocol layers. Maintained code quality with rigorous testing, code refactoring, and licensing compliance, supporting robust production deployments and rapid feature iteration.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
141Total
Bugs
24
Commits
141
Features
55
Lines of code
27,547
Activity Months8
Your Network
427 peopleWork History
June 2026
34 Commits • 11 Features
Jun 1, 2026June 2026 (2026-06) delivered a strong mix of performance improvements, stability hardening, and expanded model support for the tt-inference-server. Key operational wins include speeding rebuilds with automatic ccache enablement, smarter prefix caching with Think Tokens, and Docker image updates for the Dynamo frontend. Reliability was enhanced through test-gate hardening (stale state and proper termination sequencing) and token handling fixes, while the Dynamo protocol stack gained robustness via core improvements, thread-pool optimization, and deployment unification. Model coverage broadened significantly with onboarding of new LLMs (GPT-OSS, MiniMax, Claude skill) and additional model families (GLM-5.2, DeepSeek-V4-Pro), delivering measurable business value in throughput, stability, and capability.
34 Commits • 11 Features
Jun 1, 2026June 2026 (2026-06) delivered a strong mix of performance improvements, stability hardening, and expanded model support for the tt-inference-server. Key operational wins include speeding rebuilds with automatic ccache enablement, smarter prefix caching with Think Tokens, and Docker image updates for the Dynamo frontend. Reliability was enhanced through test-gate hardening (stale state and proper termination sequencing) and token handling fixes, while the Dynamo protocol stack gained robustness via core improvements, thread-pool optimization, and deployment unification. Model coverage broadened significantly with onboarding of new LLMs (GPT-OSS, MiniMax, Claude skill) and additional model families (GLM-5.2, DeepSeek-V4-Pro), delivering measurable business value in throughput, stability, and capability.
June 2026
May 2026
17 Commits • 4 Features
May 1, 2026May 2026 (tenstorrent/tt-inference-server): Delivered core platform enhancements across multiturn chat, streaming responses, and frontend integration, while simplifying API surface and hardening infrastructure. Key improvements include multiturn chat with cancellation and token telemetry, a unified streaming/non-streaming pipeline with a dedicated V1 Responses API, and Dynamo frontend integration with Kimi K2 tokenization and reasoning parser. Deprecated the session-based API to reduce maintenance costs. Stability and performance work across build, caching, and token handling delivered observable improvements in latency, observability, and deployment reliability.
May 2026
17 Commits • 4 Features
May 1, 2026May 2026 (tenstorrent/tt-inference-server): Delivered core platform enhancements across multiturn chat, streaming responses, and frontend integration, while simplifying API surface and hardening infrastructure. Key improvements include multiturn chat with cancellation and token telemetry, a unified streaming/non-streaming pipeline with a dedicated V1 Responses API, and Dynamo frontend integration with Kimi K2 tokenization and reasoning parser. Deprecated the session-based API to reduce maintenance costs. Stability and performance work across build, caching, and token handling delivered observable improvements in latency, observability, and deployment reliability.
April 2026
18 Commits • 5 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-inference-server highlighting key features delivered, major bugs fixed, impact, and technical skills demonstrated. This month focused on OpenAI integration, session caching enhancements, configurable performance mode, benchmarking improvements, and code quality hygiene to ensure reliability and business value.
18 Commits • 5 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-inference-server highlighting key features delivered, major bugs fixed, impact, and technical skills demonstrated. This month focused on OpenAI integration, session caching enhancements, configurable performance mode, benchmarking improvements, and code quality hygiene to ensure reliability and business value.
April 2026
February 2026
6 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-inference-server focusing on business value, reliability, and performance improvements. Key features delivered include an OpenAI-compatible Chat Completions API for the C++ server with both streaming and non-streaming modes, and a dynamic configuration system driven by environment variables for device management and service selection. Performance optimizations were achieved by migrating embedding models to Metal backends for BGE and Qwen3-Embedding-8B, removing vLLM multiprocessing and streamlining test coverage. Maintenance improvements stabilized dependencies and added CI validation with a TTNN build check to improve release reliability. A critical bug fix was deployed in the Scheduler to prevent infinite restarts of dead workers by introducing a queue index and preserving associated result queues, accompanied by regression tests to guard against future regressions. These efforts collectively reduce latency, improve throughput, and enhance maintainability, delivering measurable business value in resource utilization, inference performance, and release confidence.
February 2026
6 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-inference-server focusing on business value, reliability, and performance improvements. Key features delivered include an OpenAI-compatible Chat Completions API for the C++ server with both streaming and non-streaming modes, and a dynamic configuration system driven by environment variables for device management and service selection. Performance optimizations were achieved by migrating embedding models to Metal backends for BGE and Qwen3-Embedding-8B, removing vLLM multiprocessing and streamlining test coverage. Maintenance improvements stabilized dependencies and added CI validation with a TTNN build check to improve release reliability. A critical bug fix was deployed in the Scheduler to prevent infinite restarts of dead workers by introducing a queue index and preserving associated result queues, accompanied by regression tests to guard against future regressions. These efforts collectively reduce latency, improve throughput, and enhance maintainability, delivering measurable business value in resource utilization, inference performance, and release confidence.
January 2026
12 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for tt-inference-server (tenstorrent): Delivered end-to-end Qwen3-Embedding-8B deployment with a new runner, benchmarks, and environment upgrades, including Qwen3 env vars and Python 3.10/vLLM compatibility. Implemented governance for benchmarks, evals, and spec tests to ensure reproducibility and faster validation of embeddings. Strengthened reporting and data processing by making embedding reports robust to multiple JSON formats and clarifying metrics. Refined large-model benchmarking and performance for BGE Large and image tasks (Img2Img and Inpainting) with config refactors and increased concurrency. Hardened reliability and infrastructure with longer timeouts for SD3.5, updated docker image defaults, and transformers version fixes, improving stability in production. Key achievements focused on delivering business value: faster, more reliable deployment of embedding models; improved benchmarking fidelity and visibility into model performance; and stronger infrastructure guardrails for production workloads.
12 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for tt-inference-server (tenstorrent): Delivered end-to-end Qwen3-Embedding-8B deployment with a new runner, benchmarks, and environment upgrades, including Qwen3 env vars and Python 3.10/vLLM compatibility. Implemented governance for benchmarks, evals, and spec tests to ensure reproducibility and faster validation of embeddings. Strengthened reporting and data processing by making embedding reports robust to multiple JSON formats and clarifying metrics. Refined large-model benchmarking and performance for BGE Large and image tasks (Img2Img and Inpainting) with config refactors and increased concurrency. Hardened reliability and infrastructure with longer timeouts for SD3.5, updated docker image defaults, and transformers version fixes, improving stability in production. Key achievements focused on delivering business value: faster, more reliable deployment of embedding models; improved benchmarking fidelity and visibility into model performance; and stronger infrastructure guardrails for production workloads.
January 2026
December 2025
38 Commits • 20 Features
Dec 1, 2025Concise monthly summary for 2025-12 (tenstorrent/tt-inference-server). Focused on delivering business value through robust infra, throughput improvements, and streamlined deployment, with clear alignment to product goals and ongoing performance improvements. Highlights include foundational infra work, batching optimizations, API/embedding enhancements, and standardized evaluation/CI in support of faster iterations and reliability.
December 2025
38 Commits • 20 Features
Dec 1, 2025Concise monthly summary for 2025-12 (tenstorrent/tt-inference-server). Focused on delivering business value through robust infra, throughput improvements, and streamlined deployment, with clear alignment to product goals and ongoing performance improvements. Highlights include foundational infra work, batching optimizations, API/embedding enhancements, and standardized evaluation/CI in support of faster iterations and reliability.
November 2025
9 Commits • 5 Features
Nov 1, 2025November 2025 — Tenstorrent/tt-inference-server: focused on delivering robust SDXL and Qwen 4B workflows, streamlined deployment, and improved testing to accelerate feature delivery and reliability. Key features and improvements: - SDXL Image Editing and Inpainting with Setup Guide: Implemented a new SDXL inpainting model runner with image/mask processing, code cleanup, and a README guide for SDXL image-to-image and edit workflows. Commits include c5b570657122cb05449746894220f3e58e6d24b9 (Create New Model Runner for SDXL Inpainting) and 215943318f72637da0aa92647a5bb59a1cf38c77 (Add Instructions for Img2Img and Inpainting SDXL Setup in Readme). - Qwen 4B Embedding Enhancements: Added an embedding runner for Qwen 4B with batch processing and token-limit options; API cleanup and tokenizer support to improve text processing flexibility and performance. Commit: 7f6ba97fefbaacf6e60505e20e8d736b20fb29e2 (Add Forge vLLM Embedding Runner). - Deployment and Packaging Optimizations for Inference Server: Reduced Docker image size using multi-stage builds and packaging the vLLM Forge plugin in the image for streamlined deployment. Commits: db6efd42c0a97c2098a1dc4ed0057d439c821e92 (Reduce Docker Image Size) and 3df2d895976c989523a45f30e1981d102cf4e1bc (Package vLLM Forge plugin in Docker image). - VLLM Forge Runtime Improvements: Standardized device initialization across model runners; added minimum context length validation to Forge vLLM plugin arguments to improve robustness. Commits: debbed9a31bcb9f789eeb049039fe60e4ba9d000 and 3fa771f97d0b9df79f3a999e780ae698dadceaf2. - Maintenance and Testing Improvements: Removed deprecated TT_MESH_GRAPH_DESC_PATH and strengthened the tt-media-server testing framework to improve reliability and performance tests. Commits: d74c3bf13dc385d216df8eb00878b5b199dd7562 and b033dda1c756e43bf549a41dd6224df2b7f16822. Overall impact and accomplishments: - Expanded feature set with SDXL and Qwen 4B support, enabling richer image editing and embedding capabilities for customers. - Achieved deployment efficiency with smaller, multi-stage Docker images and streamlined Forge plugin packaging. - Improved reliability and performance through hardened device initialization, context-length validation, and strengthened testing. - Enhanced code quality and maintainability via API cleanups and PEP8-aligned refactors. Technologies and skills demonstrated: - Docker multi-stage builds and image optimization; Forge vLLM plugin integration. - Model runners and embedding pipelines (SDXL Inpainting, Qwen 4B Embeddings). - API design/cleanup, tokenizer integration, and documentation contributions. - Test automation, CI reliability improvements, and code quality refinements.
9 Commits • 5 Features
Nov 1, 2025November 2025 — Tenstorrent/tt-inference-server: focused on delivering robust SDXL and Qwen 4B workflows, streamlined deployment, and improved testing to accelerate feature delivery and reliability. Key features and improvements: - SDXL Image Editing and Inpainting with Setup Guide: Implemented a new SDXL inpainting model runner with image/mask processing, code cleanup, and a README guide for SDXL image-to-image and edit workflows. Commits include c5b570657122cb05449746894220f3e58e6d24b9 (Create New Model Runner for SDXL Inpainting) and 215943318f72637da0aa92647a5bb59a1cf38c77 (Add Instructions for Img2Img and Inpainting SDXL Setup in Readme). - Qwen 4B Embedding Enhancements: Added an embedding runner for Qwen 4B with batch processing and token-limit options; API cleanup and tokenizer support to improve text processing flexibility and performance. Commit: 7f6ba97fefbaacf6e60505e20e8d736b20fb29e2 (Add Forge vLLM Embedding Runner). - Deployment and Packaging Optimizations for Inference Server: Reduced Docker image size using multi-stage builds and packaging the vLLM Forge plugin in the image for streamlined deployment. Commits: db6efd42c0a97c2098a1dc4ed0057d439c821e92 (Reduce Docker Image Size) and 3df2d895976c989523a45f30e1981d102cf4e1bc (Package vLLM Forge plugin in Docker image). - VLLM Forge Runtime Improvements: Standardized device initialization across model runners; added minimum context length validation to Forge vLLM plugin arguments to improve robustness. Commits: debbed9a31bcb9f789eeb049039fe60e4ba9d000 and 3fa771f97d0b9df79f3a999e780ae698dadceaf2. - Maintenance and Testing Improvements: Removed deprecated TT_MESH_GRAPH_DESC_PATH and strengthened the tt-media-server testing framework to improve reliability and performance tests. Commits: d74c3bf13dc385d216df8eb00878b5b199dd7562 and b033dda1c756e43bf549a41dd6224df2b7f16822. Overall impact and accomplishments: - Expanded feature set with SDXL and Qwen 4B support, enabling richer image editing and embedding capabilities for customers. - Achieved deployment efficiency with smaller, multi-stage Docker images and streamlined Forge plugin packaging. - Improved reliability and performance through hardened device initialization, context-length validation, and strengthened testing. - Enhanced code quality and maintainability via API cleanups and PEP8-aligned refactors. Technologies and skills demonstrated: - Docker multi-stage builds and image optimization; Forge vLLM plugin integration. - Model runners and embedding pipelines (SDXL Inpainting, Qwen 4B Embeddings). - API design/cleanup, tokenizer integration, and documentation contributions. - Test automation, CI reliability improvements, and code quality refinements.
November 2025
October 2025
7 Commits • 3 Features
Oct 1, 2025October 2025 performance highlights for tenstorrent/tt-inference-server focused on delivering richer SDXL workflows, reproducibility, and deployment reliability. Delivered SDXL image generation enhancements plus a new SDXL image-to-image model runner with a base runner refactor and image router simplifications. Introduced deterministic seed control in TtSDXLPipeline for reproducible inferences via a start_latent_seed mechanism and streamlined seed handling. Updated deployment and benchmarking configurations to align Docker images and model naming conventions, improving CI/CD reliability and benchmarking reproducibility. Fixed a Scheduler device ID parsing bug to correctly handle spaces and compute worker counts, eliminating misallocation. Overall, these changes enhance user-facing capabilities, predictability, and operational stability across inference workloads.
October 2025
7 Commits • 3 Features
Oct 1, 2025October 2025 performance highlights for tenstorrent/tt-inference-server focused on delivering richer SDXL workflows, reproducibility, and deployment reliability. Delivered SDXL image generation enhancements plus a new SDXL image-to-image model runner with a base runner refactor and image router simplifications. Introduced deterministic seed control in TtSDXLPipeline for reproducible inferences via a start_latent_seed mechanism and streamlined seed handling. Updated deployment and benchmarking configurations to align Docker images and model naming conventions, improving CI/CD reliability and benchmarking reproducibility. Fixed a Scheduler device ID parsing bug to correctly handle spaces and compute worker counts, eliminating misallocation. Overall, these changes enhance user-facing capabilities, predictability, and operational stability across inference workloads.
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Quality Metrics
Correctness90.6%
Maintainability85.4%
Architecture86.6%
Performance86.4%
AI Usage36.8%
Skills & Technologies
Programming Languages
BashC++CMakeDockerfileGitJSONMarkdownPythonShellYAML
Technical Skills
AI IntegrationAI Model DevelopmentAI integrationAI interactionAI model integrationAPI DevelopmentAPI IntegrationAPI designAPI developmentAPI integrationAsynchronous programmingBackend DevelopmentBackend developmentBenchmarkingBuild Automation
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