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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.
Running Cogni-Brain on DGX Spark · Muse-Glimmer-30B-NVFP4-W4A4 + DFlash
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This repo documents my inference tuning experiments of 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 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-choicesupport --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.shdisables system swap permanently to prevent unified-memory thrashing. This is a system-level change that survives reboots.
# 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
