u/fungomix

Running Qwen3.8-Flash-Next locally on a BOSGAME M5 with:

• AMD Ryzen AI Max+ 395
• Radeon 8060S / Strix Halo
• 128 GB unified RAM
• Ubuntu 26.04
• Halogen 0.17.2
• 262K context configured
• Vision enabled

This is my Grafana monitoring dashboard during a real long-running workload, not a short synthetic benchmark.

In the screenshot:

• Decode: ~69 tok/s peak
• Prefill: ~1,155 tok/s at that moment, with peaks around 1,685 tok/s
• Context: ~84% full
• 82K tokens generated in the last hour
• GPU temperature around 62°C
• GPU power around 24 W at the captured moment
• No meaningful memory pressure despite the system showing ~121 GB physically occupied, because most of it is reclaimable model/page cache
• ~85 GB currently sitting in cache

One thing I really like about this setup is how usable Qwen3.8-Flash-Next remains with a large context and sustained agent workloads. Decode stays around the 60–70 tok/s range while prefill is still comfortably above 1k tok/s for much of the workload.

The dashboard is fed by Prometheus/Grafana and tracks Halogen throughput, KV/context usage, real vs reclaimable RAM, PSI memory pressure, GPU metrics, backend status and request activity.

Still testing it, but so far Strix Halo + unified memory is proving to be a very interesting platform for large local models.