neural-networks gpu-computing developer-tools ai-hardware

A CPU that runs entirely on GPU

GitHub - robertcprice/nCPU: nCPU: model-native and tensor-optimized CPU research runtimes with organized workloads, tools, and docs

github.com

March 4, 2026

8 min read

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61/100

Summary

nCPU is a CPU architecture that operates entirely on GPU, utilizing tensors for registers, memory, flags, and the program counter. All arithmetic operations, including addition, multiplication, bitwise operations, and shifts, are performed through trained neural networks, with specific methods like Kogge-Stone carry-lookahead for addition and learned byte-pair lookup tables for multiplication.

Key Takeaways

The nCPU operates entirely on GPU, utilizing tensors for registers, memory, flags, and the program counter, with all arithmetic performed through trained neural networks.
The nCPU achieves 100% accuracy on integer arithmetic, verified by 347 automated tests, and includes 23 models totaling approximately 135 MB.
Multiplication in the nCPU is 12 times faster than addition, contrasting with conventional CPUs where multiplication is typically slower than addition.
The nCPU benchmarks show execution times ranging from 136 to 262 microseconds per cycle, depending on the instruction mix, with models loading in 60 milliseconds.

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Coverage & discussions

Hacker News · github.com

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Analysis ready

Community Sentiment

Mixed

Positives

The exploration of running a CPU entirely on a GPU opens up intriguing possibilities for future computing architectures, potentially revolutionizing how we approach processing tasks.

Concerns

The skepticism about completely replacing CPUs with GPUs highlights fundamental differences in how each handles latency and processing, suggesting that a full transition may not be feasible.