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Muse Glimmer launcher for DGX Spark
A start/stop/status launcher that serves the NVFP4 Muse Glimmer 30B checkpoint on NVIDIA DGX Spark with vLLM, Glimmer's reasoning and tool parsers, and its DFlash speculative decoder.
Muse Glimmer 30B on DGX Spark
This launcher follows the verified vLLM DGX Spark recipe. It serves the
ModelOpt NVFP4 W4A4 checkpoint with Muse Glimmer's dedicated reasoning and tool
parsers, plus its DFlash speculative decoder.
Quick start
Use the main launcher for start, stop, status, and logs:
cd ~/muse_glimmer
./muse_glimmer.sh start
./muse_glimmer.sh status
./muse_glimmer.sh logs
./muse_glimmer.sh stop
The existing start.sh and stop.sh wrappers remain available for backward
compatibility.
A normal start reuses the tested local runtime image. This prevents a mutable
upstream image tag from breaking an otherwise working restart. To deliberately
pull the current base image and rebuild the compatibility layer:
REBUILD_IMAGE=1 ./muse_glimmer.sh start
stop retains both downloaded checkpoints and the compiler caches. A cold
start can take several minutes because model loading, compilation, FP4 tuning,
and CUDA graph capture must finish before the API becomes ready.
Check readiness with:
./muse_glimmer.sh status
The server is ready only when the command reports:
API status: ready
API endpoint
The OpenAI-compatible API is served on port 8000.
For requests from the DGX Spark itself:
http://localhost:8000/v1
For requests from another computer on the current LAN:
http://10.0.4.26:8000/v1
The served model name is:
muse-glimmer-30b
The LAN address may change if the DGX Spark receives a different address from
DHCP. Update the examples below when necessary.
API usage with curl
1. Set the API base URL
For local requests on the DGX Spark:
export MUSE_API_BASE="http://localhost:8000/v1"
For requests from another machine on the same LAN:
export MUSE_API_BASE="http://10.0.4.26:8000/v1"
Using an environment variable makes the remaining commands work with either
endpoint.
2. List served models
curl -sS "$MUSE_API_BASE/models"
Pretty-print the response with jq:
curl -sS "$MUSE_API_BASE/models" | jq
3. Check API readiness from a script
if curl -fsS "$MUSE_API_BASE/models" >/dev/null; then
echo "Muse Glimmer API is ready"
else
echo "Muse Glimmer API is not ready"
fi
4. Run a basic chat completion
Published request sampling is temperature=1.0, top_p=0.95, a




