Local LLM

DGX Spark 2x
Joined June 2013
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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…
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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.
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prefill 2300tps @ 32k
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卧槽? 4500tokne/s??? qwen3.8 flash next on 4*R9700
Did I go a bit overboard?
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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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This is how and when I will die tinyurl.com/howwillyoudie2 .
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