Marcin Brzeżański

AI Engineer at Asseco Poland · Kraków

I train speech and language models and make them fast enough for production.

At Asseco Poland, I train and deploy speech recognition and speech synthesis models for voice assistants, train and fine-tune small and large language models, and own the latency, throughput, and scaling of LLM inference on GPUs. I also build retrieval-augmented generation systems and do R&D on agentic AI.

I contribute to open-source inference and speak at Polish conferences. The questions I keep returning to start in production and end up as research: where the time in a voice agent actually goes, what can be reused between requests, and how to measure quality when the model is only one part of the system.

Interests
Efficient LLM inference
GPU performance
Training SLMs and LLMs
Speech recognition
Speech synthesis

news

Speaking at Data Science Summit about getting the most out of available infrastructure, from voice-agent optimization to GPU inference. upcoming
My fast-mode default for Qwen3.5 on SM120 GPUs shipped in basal 2.8.0, measured at 2.5–8.6× eager-mode throughput on an RTX 5060 Ti.
Two pull requests merged into basal: a StateCache fix for shared prefixes and a Prometheus metrics endpoint.
Spoke with Tomasz Makowski at XXXII Forum Teleinformatyki about generative AI that makes public statistics easier to access.
New article on Asseco’s ML blog: how ASR and TTS models work and the trade-offs behind natural, low-latency voice.
Main Stage talk at Warsaw IT Days with Michał Torzewicz: how we build an AI operations layer for hospitals so doctors can focus on patients.
Asseco Poland published my piece on why Voice AI is harder than chatbots.
Presented the architecture behind Saya.ai, Asseco’s production voice AI system, with Michał Torzewicz at dev.ai by Data Science Summit.
Two talks at TECHbreakFast After Hours: reranking in RAG with LLMs, and detecting consistency errors in documents.
Talked about embeddings and RAG at TECHbreakFast After Hours with Krystian Prokopik.

open source

basal

A language-model inference engine. My contributions cover GPU performance, cache correctness, and observability.

  1. Fast mode by default for Qwen3.5 on SM120 GPUs #114

    shipped in v2.8.0

    Select the Fast backend automatically on Blackwell-class SM120 GPUs when FLA is available. 27 benchmark runs with zero request errors, and 99.52% top-1 agreement with the fp32 eager reference.

    Short prompt, 1 question
    2.46×
    Short prompt, 5 questions
    6.32×
    Long prompt, 5 questions
    8.64×
    Figure 1. Median throughput of Fast relative to Eager, log scale, three repetitions per workload. RTX 5060 Ti 16 GB on Windows; figures from PR #114.

    GitHub lists this pull request as closed because the maintainer landed my commits b6eaf06 and a8fcba2 directly in the release, as noted on the PR.

  2. Preserve shared prefixes in StateCache #56

    merged

    The prefix index kept one entry per block hash, so caching a second branch could hide the first one’s longer match. The fix keeps every branch discoverable, selects the longest usable prefix, and is covered by regression tests and a randomized oracle.

      Reused
      0
      Computed
      0
      Prefill skipped
      0%

      Tickets, probabilities, and token counts are illustrative. Reuse changes the work, never the answer.

      Figure 2. Prefix reuse across typed-decision requests on support-ticket threads, drawn like a commit graph. Each request is a cached state plus a question, answered with a probability per allowed option in one forward pass; only the uncached part is prefilled. Before #56, caching thread B overwrote the index entry it shares with A, so a question about A matched only the shared prefix.
    1. Prometheus serving metrics #48

      merged

      A dependency-free /metrics endpoint for build, load, and state-cache statistics, collected only at scrape time.

    talks

    Conference talks and technical meetups, delivered in Polish; the original title sits under each English one. TECHbreakFast recordings need a free livetech account.

    1. DSS

      Making the most of available infrastructure: from voice-agent optimization to GPU inference

      Jak wycisnąć maksimum z dostępnej infrastruktury? Od optymalizacji agenta głosowego do inferencji na GPU

      upcoming Data Science Summit · Warsaw ·

    2. WID

      Let doctors treat: transforming hospital operations with AI

      Pozwólmy lekarzom leczyć: operacyjna transformacja szpitala dzięki AI

      Warsaw IT Days · Main Stage · with Michał Torzewicz ·

    3. dev.ai

      How we built a mature voice AI system from scratch: the architecture behind Saya.ai

      Jak stworzyliśmy dojrzały system Voice AI od podstaw: architektura stojąca za Saya.ai

      dev.ai by Data Science Summit · with Michał Torzewicz ·

    4. FTI

      From data to answers: how generative AI is changing access to public statistics

      Od danych do odpowiedzi – jak generatywna AI zmienia dostęp do statystyki publicznej

      XXXII Forum Teleinformatyki · with Tomasz Makowski · · theses (PDF)

    5. TBF

      From retrieval to perfection: reranking in RAG with LLMs

      Od retrieval do perfekcji: reranking w RAG z wykorzystaniem LLM

      TECHbreakFast After Hours by Asseco · with Przemysław Wojnar · · recording

    6. TBF

      Automatically detecting consistency errors in documents

      Automatyczna detekcja błędów spójności w dokumentach

      TECHbreakFast After Hours by Asseco · with Adrian Ćwiąkała · · recording

    7. TBF

      Embeddings and RAG: a revolution in natural language processing

      Embeddings i RAG: rewolucja w przetwarzaniu języka naturalnego

      TECHbreakFast After Hours by Asseco · with Krystian Prokopik · · recording

    writing

    Published by Asseco Poland. Articles are in Polish.

    1. ML Blog

      Voice AI od podstaw: modele ASR i TTS

      How speech recognition and synthesis work, and the trade-offs behind natural, low-latency voice systems.

      Asseco ML Blog ·

    2. Asseco

      Dlaczego Voice AI jest trudniejsze niż chatboty

      Why conversational quality depends on the whole system, from noisy audio to response generation.

      Asseco Poland ·