Build the whole system around the work AI actually performs.
Cerebral Chips is an open-source, community-built effort for local language intelligence—from workload evidence and developer tools to accelerator architecture and verification.
Early-stage architecture, prototype, and open-source research phase
Every machine should be able to think where it operates.
Useful intelligence should not always depend on a permanent round trip to the cloud. Local execution can give product teams stronger control over responsiveness, privacy boundaries, connectivity, and the economics of repeated inference.
That requires more than adding a matrix engine to a device. The model format, numerical behavior, runtime, compiler path, memory system, firmware, compute architecture, and validation process have to be designed as one measurable system.
Local AI is a system problem.
The workload is the starting point.
Prefill and decode stress a machine differently. Tensor shapes, quantization, operator mix, and memory traffic belong in the architecture conversation from the beginning.
Hardware and software share one contract.
An accelerator becomes useful only when models, runtimes, kernels, firmware, memory movement, and developer feedback can reach it coherently.
Evidence should constrain ambition.
Simulation, reference comparison, verification, and review determine what advances. Status labels keep research direction separate from completed capability.
A shared platform for a shared challenge.
Local intelligence will shape the machines people live and work with. Cerebral Chips is open to the people who want that future to be transparent, reviewable, and useful beyond any one team.
- Inspect the assumptions and artifacts behind the work
- Improve the platform through research, review, and code
- Help keep local AI accessible to the people building with it
How the team intends to build.
Measure before freezing
Use workload evidence and analytical models to narrow the architecture before hardening implementation choices.
Make boundaries explicit
Separate proposed specifications, prototypes, verified artifacts, and longer-term direction in both engineering and communication.
Design across layers
Treat model behavior, numerical formats, software interfaces, memory movement, and compute as connected decisions.
Automate with independent checks
Use agents to accelerate iteration while compilation, reference models, randomized tests, coverage, and human review gate acceptance.
Open work becomes stronger when the community can carry it forward.
The publication path includes architecture notes, workload models, software, RTL, and verification evidence where ownership, licensing, documentation, and validation status are clear. Each artifact should give contributors a useful way to understand, test, and improve the platform.
- Clear contribution paths as public repositories become ready
- No benchmark without method and limitations
- No generated artifact accepted on confidence alone
- No roadmap direction presented as shipped capability
Bring the workload, constraint, or architecture question.
Focused technical exchanges are the best place to begin.