Star on GitHub
Task-specific model development.
Konic turns repeated production behavior into compact language models trained around defined objectives.
Task boundary definition
Compact model training
Model adaptation
konic
Tailored LLMs
for on-prem production AI.
konic works directly with teams to design, evaluate, and deploy compact task-specific LLM models for production AI environments.
Production AI belongs on purpose-built models.
Model infrastructure, not workflow wrappers.
Konic builds compact language models trained, evaluated, and deployed for defined production objectives.
Task-specific intelligence with measurable boundaries.
Each model is shaped around clear behavior, benchmarked against cost, latency, quality, and control.
Smaller models for production inference.
Konic replaces repeated broad-model inference with specialized LLMs designed for efficient serving.
What Konic builds for production AI.
Book a demo
Evaluation and benchmark systems.
Measure specialized models against broad-model baselines across quality, cost, latency, and control.
Task-level benchmarks
Baseline comparison
Regression tracking
Production inference infrastructure.
Deploy smaller models for efficient, controllable serving in private or on-prem production systems.
Low-latency serving
Cost-aware inference
Private deployment
Model iteration lifecycle.
Improve specialized models through data feedback, evaluation runs, and controlled releases.
Dataset refinement
Versioned models
Continuous evaluation
Models
LLMs, tailored for production
Konic works with AI teams to build compact task-specific LLM models, evaluation harnesses, and private deployment paths for repeated production AI workloads.
Private model deployment
Deploy tailored LLMs inside customer-controlled infrastructure, including on-prem and private cloud environments.
Tailored specialization
Fine-tune, distill, and adapt compact models around a specific production task and success criteria.
Book a demo
We start with a repeated workflow, define the model boundary, and scope deployment around your infrastructure.
Research
Model work made measurable.
Konic documents evaluation design, model behavior, deployment tradeoffs, and iteration history during tailored model engagements.
Jul 20, 2026
Two Stages, Much Smaller MoE: REAP Expert Pruning Followed by AWQ INT4 Quantization
We chain two complementary compression stages on Liquid LFM2.5-8B-A1B — REAP CUDA expert pruning (32→16 experts across 22 MoE layers) then external AWQ INT4 quantization (W4A16_ASYM, group 128). The p...
Read article
Jul 3, 2026
From-Scratch AWQ INT4 Quantization on Qwen3-8B
We validate a from-scratch, pure-PyTorch AWQ implementation on Qwen3-8B: group-wise INT4 with per-channel AWQ scaling produces a 4.0× smaller model (13.9 GB → 3.5 GB linear weights) at 1.034× FP16 per...
Read article
Jun 29, 2026
REAP Expert Pruning for On-Device MoE Models
We present REAP-MLX, an Apple Silicon implementation of Router-weighted Expert Activation Pruning (REAP) for Mixture-of-Experts language models. We evaluate quality retention across compression ratios...
Read article