Correction: I found my earlier 100+ tokens/s result was inflated by an API proxy buffering tokens and delivering them in bursts, which distorted SparkDash’s client-side decode measurement.
Running GLM-5.3-Flash on 2× DGX Spark with NVIDIA Flare ✨
- 1M context window, 2.32M-token shared KV pool
- Prose decode, single stream: 117.2 tok/s
- Prose decode, 2 concurrent: 167.4 tok/s aggregate
- Prose decode, 4 concurrent: 215.8 tok/s aggregate
- 32K prefill: 2,327.9 tok/s
- Available memory after loading: ~13 GiB on head /worker each
Checkpoint: GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
github.com/NVIDIA/flare-loca…
Running GLM-5.3-Flash on 2× DGX Spark with NVIDIA Flare ✨
- 1M context window, 2.32M-token shared KV pool
- Prose decode, single stream: 117.2 tok/s
- Prose decode, 2 concurrent: 167.4 tok/s aggregate
- Prose decode, 4 concurrent: 215.8 tok/s aggregate
- 32K prefill: 2,327.9 tok/s
- Available memory after loading: ~13 GiB on head /worker each
Checkpoint: GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
github.com/NVIDIA/flare-loca…
NVIDIA just released Flare, a local inference server built for DGX Spark and RTX Blackwell GPUs
- Built for long chats, agents and tool calls
- Serves from Rust, no Python
- Plans memory up front
- FP8 KV cache by default
Interesting timing... right around when DeepSeek open-sourced their inference engine.
Link below.
推荐有DGX Spark的人都去安装!
sparkDash ⚡️ v2.0 is now LIVE!!
This is a HUGE release and a COMPLETE revamp!
Here's what's new:
User Interface & UX
- Completely redesigned UI: modern, clean, minimal, yet powerful
- Live fleet overview with VRAM, GPU, temps, power and tok/s on every card
Run models directly from sparkDash
- Add your own start/stop scripts and run your local models straight from the dashboard
- Start a model from its Spark's card and see when it's still loading
Benchmarks
- A dedicated page for each benchmark:
Decode, Prefill, Quality and Tool Eval Bench
- Built-in quality benchmarks to check how good a model really is: GSM8K, MMLU and instruction following
- Tool Eval Bench built in, with ready-made runs
- Compare two runs side by side
- More accurate Prefill tok/s
- Shareable result cards
Monitoring
- GPU history that survives restarts
- VRAM breakdown showing what the model holds, what the system uses, and what's free
- Three new pages: Token Totals, Fleet Energy and
Activity
Total tokens used
- Generated, prompt and cached tokens across your whole fleet
- Break it down by type, model or Spark
- Ranges: 24h, 7d, 30d and all time
- Cache hit rate and change vs the previous period
- Auto insights: busiest day, busiest model, and which Spark did the most work
- Sortable tables and CSV export
Fleet Energy
- kWh per node, by hour or by day
- Average power and peak draw
- Efficiency in Wh per 1,000 tokens
- Coverage, showing how much of the period was measured
- Estimated cost, using the electricity price and currency you set in Settings
Activity
- Full event history for your fleet: Sparks going online or offline, throttling, and benchmark and model start/stop events
- Filter by Spark and event type, with paging
- The latest events also appear on the Overview
And lots and lots of other cool features!
More screenshots below.
Get it here:
github.com/MiaAI-Lab/sparkDa…
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