AI engineering and infrastructure at Nuqta
What's under the surface makes the difference.
Agents, multi-model architecture and inference infrastructure we run ourselves.
AI & agents
- LLMs
- Agentic AI
- Multi-agent systems
- RAG
- Long-context AI
- AI employees
- Tool calling
- MCP
- Browser agents
- Computer-use agents
- Voice AI
- Embeddings & semantic search
Multi-model architecture
We're not tied to one model. We pick the right model for each task and each customer.
- Mibyan 4.1
- OpenAI
- DeepSeek
- Qwen
- Kimi
- Gemma
- Open-source models
AI infrastructure
- vLLM
- RunPod
- OpenRouter
- GPU inference
- H100
- L40S
- RTX PRO 6000
- FP8
- NVFP4
- Quantization
- Model serving
- High-concurrency inference
- OpenAI-compatible APIs
~111–157tokens/sec
Measured in internal tests on specific models and configurations. Not a general figure for all models.
Deployment options
- CloudFully managed by Nuqta.
- Private cloudInside your cloud environment.
- On-premiseOn your own infrastructure.
- APIBuild your products on Mibyan.
- Bring your own modelChoose the provider or model.
Mibyan Platform
An OpenAI-compatible API: change the base URL, keep your code.
# OpenAI-compatible
from openai import OpenAI
client = OpenAI(base_url=MIBYAN_BASE_URL, api_key=MIBYAN_API_KEY)
client.chat.completions.create(model="mibyan-4.1", messages=[...])