AI inference research lab
Minima compresses model weights and KV cache, then serves them with a hardware-aware runtime. Increase capacity, fit longer contexts and lower cost per token—without replacing your model or rewriting your application.
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QWEN3-32B / A100 80 GB
Published system result
Qwen3-32B · one NVIDIA A100 80 GB · bf16 · 8K context
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Figures reported in the Minima arXiv preprint submitted February 2026. Results vary by model, hardware, context and traffic shape. See the disclosed setup.
Latest from Minima
We work across the representation, memory path and serving runtime—because real inference efficiency is a systems problem.
Our preprint covers learned sensitivity, mixed Tucker/TT/TR decompositions, healing, custom kernels and speculative decoding in a single production-shaped pipeline.
Explore the paperWe are preparing hosted access for optimized open-weight models. OpenRouter provider integration is in progress.
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Explore Minima AI AgentThe mnma engine
Minima is the company. mnma is our model-optimization and inference engine: a coordinated stack for weights, cache and runtime.
Hardware-aware low-precision formats and tensor-network decompositions reduce weight memory while protecting sensitive regions.
Tiered compression preserves recent and high-value context while storing older cache pages in compact representations.
Custom CUDA kernels, scheduling and speculative decoding turn smaller representations into measurable serving gains.
Benchmark evidence
Every result is tied to a model, hardware configuration, workload and quality gate. Change the setup and the result can change.
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Products
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Minima research · arXiv:2602.01613
The preprint describes a five-stage system: learned sensitivity analysis, mixed structural decomposition, healing, custom CUDA kernel execution and speculative decoding. The central result is not a standalone compression ratio—it is an end-to-end serving result on a disclosed 32B workload.
Who Minima is for
We focus on workloads where memory, context capacity, throughput and data control have direct operating consequences.
Fit and serve open-weight or internal models against explicit latency, memory and quality gates.
Increase serving headroom before product growth turns GPU capacity into a bottleneck.
Keep model traffic within controlled environments and support existing compliance programs.
Explore practical routes to longer context, higher concurrency or fewer deployment nodes.
Company
Minima AI, Inc. builds the mnma engine, hosted inference access and private AI applications from one shared systems foundation.
Co-founder & CEO
Former Principal AI Engineer at Atlassian. Sergii has built production search, recommendation and generative-AI systems and leads Minima's product and inference strategy.
Co-founder & CTO
An AI systems architect based in California, Davyd leads Minima's model compression, kernel and runtime engineering.
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