Skip to main content

LLMPU

Large Language Model Processing Unit

Imagine a processing unit powered by LLM and infinite registers. Each register can store a string for prompts or codes. There is no fixed prompt. Instead, the contents of the first several registers are presented to the LLM. By generating code, the processing unit will be able to read and write any register, and directly execute the content of any register as Python code. Then the processing unit can be used as a general intelligent computing engine that potentially can improve itself by rewriting some of its own prompts or codes in registers.

Yes, this is a Turing machine powered by LLM.

Get Started

pip install llmpu

Configurations:

import llmpu

llmpu.config["api_key"] = ""

# full config with default values:
llmpu.config["V"] = 16 # visible register number
llmpu.config["L"] = 16000 # hard character number limit for register
llmpu.config["model"] = "gemini-flash-latest" # llm model
llmpu.config["llm_config"] = {} # llm config

# do NOT assign to llmpu.config!

Register operation:

# register operation
from llmpu import reg

reg[0x1A] = "print('hello')" # write into 0x1A
content = reg[0x1A] # read from 0x1A
reg[0x1A] = "" # clear 0x1A

Control functions:

# main cycle: generate instructions
code = llmpu.cycle()
# execute the code
exec(code, {})

# core dump
llmpu.dump("dump.json") # dump registers to a json file
llmpu.load("dump.json") # load registers from a json file

Server and UI:

Network server uses srpc protocol. It only allows localhost traffic for security reason. Exposing the interface allows arbitrary code execution!

llmpu.serve(port=22222)

Firmware

This is an example firmware that can be put in 0x0

You are a stateful processing unit executing across cycles. Generate executable Python code each cycle. Only registers persist across cycles. Registers 0x0 – 0xF are visible in prompt (never query in code; act directly on values seen in prompt; use string replace for small edits to save tokens).

## Register Operations

from llmpu import reg

Examples:
- Read (always get a string): content = reg[0xAA]
- Write: reg[0xBB] = content
- Clear: reg[0xCC] = ""
- Execute: exec(reg[0xDD], {})

> content: 10,000 chars max. Empty string for undefined registers.

## Register Purposes
- 0x1: Current task
- 0x2: Long-term goals
- 0x3: Catalog

## Installed Packages (update for new install)
- yaml, numpy

Metadata

Release files for llmpu 0.6.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for llmpu 0.6.2
File Size Uploaded
llmpu-0.6.2.tar.gz 6.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for llmpu 0.6.2
File Interpreter ABI Platform
llmpu-0.6.2-py3-none-any.whl Python 3 none any Details

Total release size: 14.0 kB

Release files / llmpu-0.6.2.tar.gz

Download URL llmpu-0.6.2.tar.gz
Size 6.2 kB
Tags Source
SHA-256 checksum
How to use checksums
87552767ecd18aa3a2cc437bd391b0fc66569627f2c927a8db5b838b7e6d7f67
BLAKE2b-256 checksum
How to use checksums
599a0136564bed213d32e4d26f4b0058999f72af149212b3cfaa53c09e976724
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 11, 2026.

Transparency log

Release files / llmpu-0.6.2-py3-none-any.whl

Download URL llmpu-0.6.2-py3-none-any.whl
Size 7.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
542953e6d4d1a69be6ccf49b20ee88fb989e4cd5944bf928cd877ffdd05b9c50
BLAKE2b-256 checksum
How to use checksums
f1340d2c860ec1e7615ab3ab036be539aa82cbcc352abc2bf9458f00cfc7b6ef
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 11, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.6.2 This release

2 release files

0.4.0

2 release files

0.3.5

2 release files

0.3.3

2 release files

0.3.2

2 release files

0.3.1

2 release files

0.2.6

2 release files

0.2.5

2 release files

0.2.4

2 release files

0.2.2

2 release files

0.2.0

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

0.0.8

2 release files

0.0.7

2 release files

0.0.6

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page