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FoundersFounders
3309

Andrey Mikhaylov

Logo
Andrey Mikhaylov
CEO & Co-Founder of
TurboFieldfare (2026)
About

Andrey Mikhaylov is the creator of TurboFieldfare, a groundbreaking open-source local inference engine built in Swift and Metal that runs Google's Gemma 4 26B-A4B model in approximately 2 GB of RAM on Apple Silicon Macs.

Before founding

Andrey is a Senior iOS and Metal Engineer currently at Lapse and previously at Prisma AI, based in London, UK, with deep expertise in images, video, and on-device AI.

Founder Insights

Andrey engineered TurboFieldfare from scratch using Apple's Metal GPU framework and Swift, conducting over 100 optimization experiments to achieve its unprecedented memory efficiency.

Experience

TurboFieldfare hit #1 on Hacker News on July 29, 2026 with 218 points, and delivers 5-6 tokens per second on an M2 Air and 31-35 tokens per second on an M5 Pro.

Scale & Impact

The project democratizes access to large language models by enabling a 26-billion-parameter model to run on an 8 GB MacBook Air, offering a native app, CLI, and an OpenAI-compatible local server.

3309

Andrey Mikhaylov

TurboFieldfare
Founder
Logo
Andrey Mikhaylov

Company(s)

TurboFieldfare

Industry

SaaS

Year Founded

2026

Stage/Funding

N/A

Company Location

gb

Connect

About

Andrey Mikhaylov is the creator of TurboFieldfare, a groundbreaking open-source local inference engine built in Swift and Metal that runs Google's Gemma 4 26B-A4B model in approximately 2 GB of RAM on Apple Silicon Macs.

Before founding

Andrey is a Senior iOS and Metal Engineer currently at Lapse and previously at Prisma AI, based in London, UK, with deep expertise in images, video, and on-device AI.

Founder Insights

Andrey engineered TurboFieldfare from scratch using Apple's Metal GPU framework and Swift, conducting over 100 optimization experiments to achieve its unprecedented memory efficiency.

Experience

TurboFieldfare hit #1 on Hacker News on July 29, 2026 with 218 points, and delivers 5-6 tokens per second on an M2 Air and 31-35 tokens per second on an M5 Pro.

Scale & Impact

The project democratizes access to large language models by enabling a 26-billion-parameter model to run on an 8 GB MacBook Air, offering a native app, CLI, and an OpenAI-compatible local server.

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