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toverainc
willow-inference-server
Python2023–2025
toverainc / willow-inference-server

willow-inference-server

Open source, local, and self-hosted highly optimized language inference server supporting ASR/STT, TTS, and LLM across WebRTC, REST, and WS

AI & Machine LearningSystems & InfrastructureSecurity & PrivacySelf-hostedLearning & ReferenceNetworking
Explore on GitHubRead Hacker News discussions
2Distinct people
11Mentions
0Explicit recommenders
511GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Open source, local, and self-hosted highly optimized language inference server supporting ASR/STT, TTS, and LLM across WebRTC, REST, and WS

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 6:01:32 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month11 dated mentions

Observed mentions: 2023-05-23 14:22:20 UTC to 2025-03-16 19:16:09 UTC.

01232023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsMay 20232023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 20242024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC211
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC303
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC202
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC101
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC000
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC101
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC101
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC000
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC000
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC000
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC000
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC000
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC101

What Hacker News says

3 sources
kkielhofner·Jun 21, 2023
We run into this constantly with Willow[0] and the Willow Inference Server[1]. There seems to be a large gap in understanding with many users. They seem to find it difficult to understand a fundamental reality: GPUs are so physically different and better suited to many/most ML tasks all the CPU tricks in the world cannot bring CPU even close to the performance of GPUs (while maintaining quality/functionality) for many tasks. I find this interesting because everyone seems to take it as obvious that integrated graphics vs discrete graphics for gaming aren't even close. Ditto for these tasks. With Willow Inference Server I'm constantly telling people: a six year old $100 Tesla P4/GTX 1070 walks all over even the best CPUs in the world for our primary task of speech to text/ASR - at dramatically lower cost and power usage. Seriously - a GTX 1
Read the rest
070 is at least 5x faster than a Threadripper 5955WX. Our goal is to provide an open-source commercial voice assistant equivalent user experience and that is and will be fundamentally impossible for the foreseeable future on CPU. Slight tangent but there are users in the space who seem to be under the impression that they can use their Raspberry Pi for voice assistant/speech recognition. It's not even close to a fair fight but with the same implementation and settings a GTX 1070 is roughly 90x (nearly two orders of magnitude) faster[2] than a Raspberry Pi... Yes, all-in a machine with a GTX 1070 uses and order of magnitude more power (3w vs 30x) than a Raspberry Pi but the power cost in even countries with the most expensive power in the world results in a $2-$3/mo cost difference - which I feel, at least, is a reasonable trade-off considering the dramatic difference in usability (Raspberry Pi is essentially useless - waiting 10-30 seconds for a response makes pulling your phone out faster). [0] - https://github.com/toverainc/willow [1] - https://github.com/toverainc/willow-inference-server [2] - https://github.com/toverainc/willow-inference-server/tree/wi...
In vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention
kkielhofner·Jun 7, 2023
Shameless plug, I'm the founder of Willow[0]. In short you can: 1) Run a local Willow Inference Server[1]. Supports CPU or CUDA, just about the fastest implementation of Whisper out there for "real time" speech. 2) Run local command detection on device. We pull your Home Assistant entities on setup and define basic grammar for them but any English commands (up to 400) are supported. They are recognized directly on the $50 ESP BOX device and sent to Home Assistant (or openHAB, or a REST endpoint, etc) for processing. Whether WIS or local our performance target is 500ms from end of speech to command executed. [0] - https://github.com/toverainc/willow [1] - https://github.com/toverainc/willow-inference-server
In GGML – AI at the Edge
kkielhofner·Jun 6, 2023
A 15 year head start in a category they essentially defined plus an entire generation of executives, developers, and users doesn’t hurt either. People complain about the “Nvidia tax” but the hardware is superior (untouchable at datacenter scale) and the “tax” turns into a dividend as soon as your (very expensive) team spends hour after hour (week after week) dealing with issues on other platforms compared to anything based on CUDA often being a Docker pull away with absolutely first class support on any ML framework. Nvidia gets a lot of shade on HN and elsewhere but if you’ve spent any time in this field you completely understand why they have 80-90% market share of GPGPU. With Willow[0] and the Willow Inference Server[1] I'm often asked by users with no experience in the space why we don't target AMD, Coral TPUs (don't even get me start
Read the rest
ed), etc. It's almost impossible to understand "why CUDA" unless you've fought these battles and spent time with "alternatives". I’ve been active in the space for roughly half a decade and when I look back to my early days I’m amazed what a beginner like me was able to do because of CUDA. I still routinely am. What you’re able to actually accomplish with a $1000 Nvidia card and a few lines with transformers and/or a Docker container is incredible. That said I am really looking forward to Apple stepping it up here - I’ve given up on AMD ever getting it together on GPGPU and Intel (with Arc) is even further behind. The space needs some real competition somewhere. [0] - https://github.com/toverainc/willow [1] - https://github.com/toverainc/willow-inference-server
In Nvidia releases new AI chip with 480GB CPU RAM, 96GB GPU RAM

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