COcomputerex·Mar 10, 2026
Amazing, this is what I am trying to do with https://github.com/computerex/dlgo
In Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Siliconcomputerex
dlgo
C2026–2026
computerex / dlgo
dlgo
Golang inference engine and deep learning primitives
3Distinct people
12Mentions
0Explicit recommenders
46GitHub stars
What to know before exploring
What it is
Golang inference engine and deep learning primitives
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
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:00:59 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 month12 dated mentions
Observed mentions: 2026-03-07 00:49:20 UTC to 2026-10-18 02:08:39 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 5 | 2 | 3 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 1 | 0 | 1 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2026 | 2026-09-01 00:00:00 UTC | 2026-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2026 | 2026-10-01 00:00:00 UTC | 2026-11-01 00:00:00 UTC | 5 | 1 | 4 |
What Hacker News says
5 sourcesYou can use my new golang inference engine to run variants of Qwen 3.5 faster than llama.cpp:
https://github.com/computerex/dlgo
In How to run Qwen 3.5 locallyhttps://github.com/computerex/dlgo
Golang inference engine from scratch that can run a bunch of models with vulkan acceleration.
In Ask HN: What Are You Working On? (March 2026)Show HN: Go LLM inference with a Vulkan GPU back end that beats Ollama's CUDA
dlgo is an LLM inference engine written in Go. CPU path has zero dependencies beyond the standard library. GPU path uses Vulkan compute — no CUDA required.
I benchmarked it against Ollama using the exact same GGUF files on an RTX 4070 Ti SUPER:
GPU (dlgo Vulkan vs Ollama CUDA):
Qwen3.5 0.8B: 239 tok/s vs 187 tok/s — 28% faster
Gemma 3 270M: 456 tok/s vs 503 tok/s (−9%)
SmolLM2 360M: 420 tok/s vs 451 tok/s (−7%)
10 models tested, within 7–25% of CUDA on standard architectures
CPU (dlgo vs Ollama, same GGUF):
6 of 10 models within 9% of Ollama
2 models faster (Gemma 270M +3%, SmolLM2 360M +7%)
The Qwen3.5 result surprised me. Qwen3.5 uses a hybrid Gated Delta Net + attention architecture (SSM layers with a recurrent delta rule). I wrote 6 custom Vulkan compute shaders for it — conv1d, delta rule recurrence, L2 normalization, sigmoid gating
Read the rest
— and the fused Vulkan pipeline ended up outperforming llama.cpp's CUDA kernels.
Vulkan means this runs on AMD, Intel, and mobile GPUs too — not just NVIDIA. Ollama's own Vulkan backend is 66–126% slower than dlgo on the models I tested.
Supports LLaMA, Qwen2/3/3.5, Gemma, Phi, SmolLM2, Mistral, plus Whisper speech-to-text. 25+ quantization formats (Q4_0 through Q8_0, all K-quants).
Three lines to run:
model, _ := dlgo.LoadLLM("model.gguf")
response, _ := model.Chat("", "What is the capital of France?")
fmt.Println(response)
Show HN: I wrote an LLM inference engine in pure Go – 48 tok/s zero dependencies
dlgo is a pure Go deep learning inference engine. It loads GGUF models and runs them on CPU with no dependencies beyond the standard library (SIMD acceleration is optional via CGo).
I built this because I wanted to add local LLM inference to a Go project without shelling out to Python or linking against llama.cpp. The whole thing is go get github.com/computerex/dlgo and you're running models.
It supports LLaMA, Qwen 2/3/3.5, Gemma 2/3, Phi-2/4, SmolLM2, Mistral, and Whisper speech-to-text. Architectures are expressed as a declarative per-layer spec resolved at load time, so adding a new model family is mostly just describing its layer structure rather than writing a new forward pass.
Performance on a single CPU thread with Q4_K_M quantization: ~31 tok/s for LLaMA 3.2 1B, ~48 tok/s for Qwen3 0.6B, ~16 tok/s for Qwen3.5 2B (which has a hybr
Read the rest
id attention + Gated Delta Network architecture). Not going to beat llama.cpp on raw speed, but it's fast enough to be useful and the ergonomics of a native Go library are hard to beat.
Supports 25+ GGML quantization formats (Q4_0 through Q8_0, all K-quants, I-quants, F16, BF16, F32). The GGUF parser, dequantization, tokenizer, forward pass, and sampling are all implemented from scratch.
Code: https://github.com/computerex/dlgo