Cosmonomic / PulsarLM

An LLM inference engine for persistent chat bots and agents. It allows models to forget old and unimportant tokens to keep context small and inference fast. Best when paired with a memory system for recalling forgotten context.

States are kept in an LRU cache in pinned host memory (default 4 GiB and not persisted).

Only a handful of smaller and older models are implemented right now, but newer models are on the roadmap!

How it works

PulsarLM is inspired by how short-term memory forgets. During inference, custom attention kernels accumulate the attention mass of each token page. At regular intervals, pages that received little attention are removed from the context, while the system prompt and the most recent pages are always kept. Forgotten information can be recovered by pairing PulsarLM with an agent harness that recalls memories. This keeps the context window bounded, reducing the hardware needed for very long sessions.

Removing past tokens from the context breaks the prefix cache that most engines rely on. To work around this, PulsarLM stores snapshots of the context at regular intervals in an external store, and resumes a conversation from its latest snapshot.

Performance

On an RTX 5090, in bf16 with the default settings:

Prefill is measured on a 5k-token prompt. Decode is one request generating 512 tokens, and 16 concurrent requests generating 256 tokens each.

Quickstart

pip install pulsarlm

# list and download implemented models
pulsarlm models
pulsarlm download gemma-4-e2b-it

# start a chat
pulsarlm chat gemma-4-e2b-it

# or start an OpenAI compatible API server
pulsarlm serve gemma-4-e2b-it

Requirements

• Linux on x86_64, glibc >= 2.34 (Ubuntu 22.04+, Debian 12+, RHEL 9+)
• NVIDIA GPU with compute capability >= 8.0 (Ampere or newer)
• NVIDIA driver >= 580 (CUDA 13)
• Python 3.13

If installing from sdist or building from source:

• nvcc >= 13.4
• gcc >= 15
• xmake
• OpenSSL 3 headers

Development

Requires uv and the source build requirements above. To get started:

uv sync

Disclaimer

This project has only been tested on x86_64 with an RTX 5090. Wheels for other GPUs are built but untested.