A Distributed Inference Framework Enabling Running Models Exceeding Total Memory
Mood
informative
Sentiment
positive
Category
startup_launch
Key topics
Distributed Inference: A Distributed Inference Framework for Running Models Exceeding Total Memory
Snapshot generated from the HN discussion
Discussion Activity
Light discussionFirst comment
N/A
Peak period
1
Hour 1
Avg / period
1
Key moments
- 01Story posted
Nov 26, 2025 at 4:18 PM EST
4h ago
Step 01 - 02First comment
Nov 26, 2025 at 4:18 PM EST
0s after posting
Step 02 - 03Peak activity
1 comments in Hour 1
Hottest window of the conversation
Step 03 - 04Latest activity
Nov 26, 2025 at 4:18 PM EST
4h ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
We fuse pipelined-ring parallelism, disk streaming and UMA-aware scheduling so “out of memory” stops being the limit.
https://github.com/firstbatchxyz/dnet?tab=readme-ov-file
In alpha, we ship a pipelined-ring strategy inspired by PRIMA.CPP. dnet’s solver (distilp) extends it so devices can punch above memory: layers stream from disk mid-round and overlap with compute, so total model size can exceed total cluster RAM.
Please let us know if you have any questions or feedback!
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.