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Muse Glimmer NVFP4 on DGX Spark

Inference tuning notes for serving Muse Glimmer 30B NVFP4 with DFlash on a single NVIDIA DGX Spark as a consistent agent backend; the repo reports 27.5 tok/s average and 90/100 on its tool eval with 128K context.

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# Running Cogni-Brain on DGX Spark · Muse-Glimmer-30B-NVFP4-W4A4 + DFlash

![Python](https://img.shields.io/badge/python-3.10%2B-blue?logo=python&logoColor=white)
![Base Model](https://img.shields.io/badge/base%20model-Muse--Glimmer--30B--NVFP4--W4A4-cyan)
![Speculative](https://img.shields.io/badge/speculative-DFlash%20(K%3D16)-purple)
![Runtime](https://img.shields.io/badge/runtime-vllm%2Fvllm--openai%3Amuse--glimmer-orange)
![Hardware](https://img.shields.io/badge/hardware-NVIDIA%20DGX%20Spark-brightgreen?logo=nvidia&logoColor=white)
![Context](https://img.shields.io/badge/context-128K-blue)
![Tool Eval](https://img.shields.io/badge/tool--eval-90%2F100-success)
![Reasoning](https://img.shields.io/badge/reasoning-muse__glimmer-black)
![Quantization](https://img.shields.io/badge/quantization-NVFP4%20W4A4-purple)

This repo documents my inference tuning experiments of [Inferact/Muse-Glimmer-30B-NVFP4-W4A4](https://huggingface.co/Inferact/Muse-Glimmer-30B-NVFP4-W4A4) on a single DGX Spark.

> ⚠️ **Personal workstation setup. Not for enterprise use. Use at your own risk.**

---

## What is Muse-Glimmer?

[Muse-Glimmer-30B-NVFP4-W4A4](https://huggingface.co/Inferact/Muse-Glimmer-30B-NVFP4-W4A4) is Inferact's 30B reasoning and tool-calling model, served here as **Cogni-Brain** — a model alias that stays consistent across agent frameworks (Claude Code, Continue, Open WebUI, etc.) regardless of which model is running underneath.

Key characteristics:
- **NVFP4 W4A4 (Weight + Activation Quantization)** — Both weights and activations are quantized to FP4/INT4, targeting Grace-Blackwell Tensor Cores on the DGX Spark
- **muse_glimmer** tool-call and reasoning parsers (native, not adapted)
- **128K token context window** (`--max-model-len 131072`)
- Native `--enable-auto-tool-choice` support
- `--generation-config auto` — model-native generation config (no manual override)
- **30B parameter** dense model — significantly faster TPS than the 118B Laguna MoE

---

## Quick Start

> ⚠️ **Warning:** `setup/install.sh` disables system swap permanently to prevent unified-memory thrashing. This is a system-level change that survives reboots.

```bash
# 1. Set your Hugging Face token (model may be gated)
export HF_TOKEN="your_hf_token_here"

# 2. System prerequisites (swap disable, uv install, docker check)
bash setup/install.sh

# 3. Download model weights (one-time, ~60 GB)
b

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