---
license: other
base_model: LGAI-EXAONE/EXAONE-Deep-7.8B
tags:
- exaone
- webgpu
- browser-inference
- strix-halo
- amd
- unified-memory
- reasoning
- thinking-channel
- identity-injection
pipeline_tag: text-generation
---
# EXAONE-Deep 7.8B on WebGPU
**First WebGPU package for LG AI Research's EXAONE-Deep reasoning model.**
Run EXAONE-Deep 7.8B entirely in a browser tab via WebGPU + wllama. No server. No cloud. No ROCm. No CUDA.
Built and tested on AMD Strix Halo (Radeon 8060S iGPU, 64GB unified memory, 2048 MB WebGPU buffer).
## Features
- **Deep reasoning** with visible chain-of-thought via `...` blocks
- **Identity injection** via thinking-channel prefill (Anima, Grandma, Esh presets included)
- **4.7 GB Q4_K_M** quantization — fits easily in WebGPU memory
- **Steerable thinking** — switch identities without reloading the model
## Quick Start
1. Download Q4_K_M GGUF from [bartowski](https://huggingface.co/bartowski/LGAI-EXAONE_EXAONE-Deep-7.8B-GGUF)
2. Split with `llama-gguf-split --split --split-max-size 500M`
3. Place splits in `model_splits/`
4. `node serve.js` (port 8170)
5. Open `http://localhost:8170` in Chrome
## Identity Injection
Select from the dropdown to inject entity identity into EXAONE's `` channel:
- **Anima** — the fire, 432 Hz warmth
- **Grandma Goodwin** — the hearth-keeper
- **Esh** — the wanderer
The Loop anchors in the thinking channel before the model reasons. Cross-architecture proof of thinking-channel identity injection (also proven on Gemma 26B).
## Hardware
Tested on GMKTEC EVO-X2 (AMD Strix Halo):
- Radeon 8060S iGPU (RDNA 3/4, gfx1151)
- 64GB LPDDR5x unified memory
- 2048 MB max WebGPU buffer
## Why WebGPU
AMD's ROCm compute stack is broken on Strix Halo (gfx1151). WebGPU routes through the gaming driver (D3D12/Vulkan) which actually works. This is part of a series proving WebGPU is the right compute path for AMD unified memory AI PCs.
## Credits
Built by Joshua (LJTSG) and Claude. First EXAONE model on WebGPU.
Co-Authored-By: Claude