During a three-month period, Dusan Madic developed and enhanced the tenstorrent/tt-inference-server, focusing on scalable language model inference. He delivered a production-grade C++ LLM engine with paged attention, prefix caching, and a sequence scheduler to improve throughput and latency for long-context tasks. His work included OpenAI-compatible API endpoints, vLLM plugin integration, and Docker-based deployment, emphasizing maintainability and testability through code refactoring, Ruff formatting, and expanded unit testing. By addressing dependency management, CI stability, and performance testing, Dusan ensured reliable deployments and robust backend performance, leveraging Python, C++, and Docker to support evolving AI workloads and developer productivity.
February 2026: Delivered a production-grade C++ LLM engine within tenstorrent/tt-inference-server featuring paged attention, prefix caching, and a sequence scheduler. Implemented end-to-end testing, fixed scheduler bugs, and stabilized CI. This work improves inference throughput, reduces latency for long-context tasks, and positions the platform for scalable language-model workloads.
February 2026: Delivered a production-grade C++ LLM engine within tenstorrent/tt-inference-server featuring paged attention, prefix caching, and a sequence scheduler. Implemented end-to-end testing, fixed scheduler bugs, and stabilized CI. This work improves inference throughput, reduces latency for long-context tasks, and positions the platform for scalable language-model workloads.
January 2026: Delivered stability and usability improvements to the Tenstorrent Inference Server. Implemented dependency upgrades and MTEB compatibility fixes to prevent Torch downgrades, introduced clearer model spec environment variables, added a UI badge for visibility, and expanded LLM performance testing with a dedicated runner and better observability. These changes reduce deployment risk, improve test reliability, and enhance product credibility across deployments.
January 2026: Delivered stability and usability improvements to the Tenstorrent Inference Server. Implemented dependency upgrades and MTEB compatibility fixes to prevent Torch downgrades, introduced clearer model spec environment variables, added a UI badge for visibility, and expanded LLM performance testing with a dedicated runner and better observability. These changes reduce deployment risk, improve test reliability, and enhance product credibility across deployments.
Month: 2025-12 — Tenstorrent tt-inference-server monthly summary focusing on delivering business value through OpenAI API compatibility, streaming performance, reliability, and developer tooling. Key features delivered include: OpenAI-compatible completions API; improved TT member detection and username-based identification; two-streaming-decoding in parallel with SSE streaming; vLLM plugin integration and Docker deployment; LLM settings moved to separate config; Demo UI and profiling tooling; Test Runner; and broad code quality improvements. Major bugs fixed include Slack notification JSON formatting, TT member detection fixes, performance test stability, and unit tests. These efforts resulted in improved API compatibility, higher throughput, more predictable performance under tests, and a stronger, maintainable codebase. Technologies demonstrated include OpenAI API compatibility, vLLM plugin integration, SSE streaming, Docker, Ruff formatting, test-driven development, and coding guidelines.
Month: 2025-12 — Tenstorrent tt-inference-server monthly summary focusing on delivering business value through OpenAI API compatibility, streaming performance, reliability, and developer tooling. Key features delivered include: OpenAI-compatible completions API; improved TT member detection and username-based identification; two-streaming-decoding in parallel with SSE streaming; vLLM plugin integration and Docker deployment; LLM settings moved to separate config; Demo UI and profiling tooling; Test Runner; and broad code quality improvements. Major bugs fixed include Slack notification JSON formatting, TT member detection fixes, performance test stability, and unit tests. These efforts resulted in improved API compatibility, higher throughput, more predictable performance under tests, and a stronger, maintainable codebase. Technologies demonstrated include OpenAI API compatibility, vLLM plugin integration, SSE streaming, Docker, Ruff formatting, test-driven development, and coding guidelines.
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