Frontier AI models
on every device
Extreme low-bit quantization and model × hardware co-optimization bring cloud-class models to chips, devices and private networks. Smaller, faster, and your data stays local.
Same chip,
bigger models
Our in-house quantization spans INT4 down to 1-bit. Combined with architecture-aware mixed precision and hardware–software co-optimization, it brings language, vision and speech models onto edge chips.
Smaller models
at 1-bit
Accuracy loss
with A8W4 quantization
Classes of compute platforms
from cloud to edge
Milliwatt-class offline voice
on low-power chips
From 1.5B to 100B+ MoE
Very large models such as DeepSeek V4.1 Flash now run locally, and multi-stage quantization has been delivered on Chinese AI chips with tightly controlled accuracy loss.
Visual perception models
Fast, real-time inference on edge compute modules and Chinese edge chips.
Voice interaction models
Fully offline and real-time on low-power chips.
Local AI agents
Data stays in-house
An enterprise AI agent platform that runs fully offline. Models, skills and business data all live on your own hardware, local tokens are unlimited, and sensitive data never leaves your network. Deploy it on an intranet server or as a Windows, Linux or macOS client.
Local first
Cloud capabilities only when you call for them.
No token meter
No per-token billing, so long-running tasks just run.
No network needed
Works on intranets and in air-gapped environments.
Data stays on device
Contracts, financials and production data never go to the cloud.
Agentic SOP pipeline
Business know-how becomes repeatable workflows, with multiple agents producing around the clock.
ManageAgent governance
Human-in-the-loop review that watches data, anomalies and risk. Every step is auditable and traceable.
Self-improving flywheel
Local low-bit models are post-trained on your business data, so agents get better the more you use them.
One method,
cloud to edge
Our in-house inference engine runs on Apple silicon and NVIDIA platforms. Low-bit kernels, elastic expert memory and speculative decoding make large models run faster on smaller devices.
Memory for a 27B 1-bit model
on a desktop AI supercomputer
A 113 GB model
running on a 48 GB laptop
Faster 1-bit decoding
vs. the official runtime · desktop workstation
From the data center
to milliwatt chips
From LLMs and VLMs to vision, speech and other AI models, one quantization and co-optimization method has been adapted and delivered across many classes of compute.
Start with a joint pilot
Your model × your chip, or one of your business lines. Validate first, then scale.
Joint pilot
We start from your model and target hardware and prove quality and performance in real scenarios first.
Long-term co-development
Deep model–hardware co-optimization that ships with your product and grows with it.
Private deployment
Deployed on your intranet or chosen hardware, with agent capabilities tailored to your business and delivered jointly with integrators.
比特至简
智能无界
Our core team comes from technology companies such as Huawei and Alibaba and from renowned research institutes, with long-standing focus on low-bit neural networks and high-performance AI model optimization.
info@gruenbit.cn