This system delivers strong local AI inference performance while maintaining excellent power efficiency and acoustics.
The test was conducted with Qwen3.8 Flash-Next, UD-Q4_K_XL, running on an AMD Ryzen AI Max+ 395 with 16 cores and 32 threads, paired with the integrated AMD Radeon 8060S (Strix Halo) GPU and 96 GiB of GPU memory. The system was running Fedora Linux 44 with gufo-runtime 0.15.0 in a container environment.
For this test, the system was configured in Silent BIOS mode, with MTP speculative decoding using 7 draft tokens, prompt caching enabled, and the fabric clock pinned for consistent performance.
Inference Performance
On ordinary prose generation, the system achieved a median decode speed of 37.7 tokens per second.
Across different prompt lengths, decode performance remained stable:
- 3,803 prompt tokens: 1,466 tok/s prefill, 34.8 tok/s decode
- 15,048 prompt tokens: 1,489 tok/s prefill, 39.0 tok/s decode
- 30,008 prompt tokens: 1,476 tok/s prefill, 39.8 tok/s decode
- 59,977 prompt tokens: 1,470 tok/s prefill, 36.7 tok/s decode
This means the system maintained approximately 1,470–1,490 tok/s prefill performance even with prompts approaching 60K tokens.
The median decode speed increased to 75.5 tok/s on repetitive text, while a sustained-load test across 23 requests achieved a median decode speed of 38.4 tok/s.
The system also passed the 30,030-token needle test, achieving 1,444 tok/s prefill, demonstrating reliable long-context retrieval performance. It achieved 11/11 on factual questions, 2/2 on vision checks, and successfully passed the tool-call test.
Power and Thermal Efficiency
Under sustained AI inference load, GPU power consumption averaged 80.0 W, with a maximum of only 81.1 W. The maximum GPU edge temperature was 59°C.
In Silent BIOS mode, my testing shows that the system can retain approximately 95% of its inference performance while reducing power consumption by around 40% compared with the higher-power configuration.
At the same time, system noise remained around 53–56 dB, providing a comfortable acoustic profile for sustained local AI workloads.
Multi-Session Performance
The system can also handle multiple concurrent AI sessions.
With one active session, the system achieved approximately 37 tok/s. With two simultaneous sessions, each session delivered approximately 28 tok/s, for a combined throughput of 55 tok/s.
A full 262,144-token context window was tested successfully in a single session, using approximately 90.8 GiB of GPU memory. Two simultaneous sessions with 131,072 tokens per session used approximately 91.0 GiB of GPU memory.
The Result
With Qwen3.8 Flash-Next UD-Q4_K_XL, the Ryzen AI Max+ 395 and Radeon 8060S platform demonstrates that high-performance local AI does not necessarily require a high-power discrete GPU.
With around 80 W sustained GPU power, the system delivers approximately 37.7 tok/s on ordinary text generation, up to 1,489 tok/s prefill, supports a 262K-token context window, and can run two concurrent AI sessions while maintaining practical throughput.
This makes the platform particularly well suited for local LLM inference, long-context workloads, AI assistants, vision tasks, and privacy-focused on-device AI applications.