Qwen3 30B-A3B-Instruct-2507 / ROCm 7.2.4 native PP512 TG128 d0

Primary result: https://github.com/bkpaine1/halo-llamacpp/blob/3617c72a325d03f73da0764d6b64d386e2db1a9c/bench/qwen3-30b-a3b.md Pinned campaign README: https://github.com/bkpaine1/halo-llamacpp/blob/3617c72a325d03f73da0764d6b64d386e2db1a9c/README.md Model identity is the README Qwen3-30B-A3B-Instruct-2507 plus raw qwen3moe 30B.A3B Q4_K - Medium, 30.53B parameters. Native model tensor-storage size 17.28 GiB comes from llama_model_size; it is not exact disk bytes or resident memory. The commented Qwen3-30B GGUF filename in bench.sh is a TODO example, not measured publisher identity. Tested GGUF filename, publisher, repository revision and hash are unknown. Headline uses the same d0 ROCm arm: PP843.25 ±5.39 and TG73.74 ±0.17 tok/s. At d4096, PP209.17 ±0.17 and TG47.48 ±0.08. Depth4096 is pre-existing KV state, not a 4096-token timed prefill. Requested n_ctx equals depth+work:512/128 at d0 and4608/4224 at d4096; allocated capacity unknown. First-party bkpaine1 measurements dated 2026-07-23. Source repository https://github.com/bkpaine1/halo-llamacpp, capture revision 3617c72a325d03f73da0764d6b64d386e2db1a9c. Historical qualification of previously deferred evidence; no new measurement or source-change claim. Hardware: source README identifies AMD Ryzen AI Max+ 395, Radeon 8060S gfx1151 and 128 GB unified memory. Its explicit statement that every run enumerates one ROCm device, with Device 0 AMD Radeon Graphics/gfx1151, binds GPU quantity 1 to this native campaign. This is a common campaign-level device snippet, not a repeated device block in each result file. Vendor SKU specification confirms 16 CPU cores, 32 hardware threads and 40 graphics compute units: https://www.amd.com/en/products/processors/laptop/ryzen/ai-300-series/amd-ryzen-ai-max-plus-395.html . Actual benchmark thread count is not reported. Host label AI-Strix; manufacturer, machine model, distribution, kernel, memory speed and tested power setting unknown. The enumerated 65536 MiB VRAM is driver-reported GPU-addressable capacity in the unified pool, not 64 GiB additional physical memory or measured residency. Model/KV/process resident memory and exact disk bytes unknown. Runtime: llama.cpp commit 1425386fd996511e1f3295e7366c38289a92a271, ROCm/HIP 7.2.4 (hipconfig 7.2.53211), ROCm clang-22. Source common command: ROCBLAS_USE_HIPBLASLT=1 llama-bench -m <model> -ngl 999 -fa 1; build enables HIP, gfx1151, rocWMMA FATTN and HIP_NO_VMM. The raw tables report build 1425386 (1), ROCm, ngl999 and fa1. Build number 1 is reported literally, not a modern release tag. No speculative drafter in these native runs. Live services shared this working box, so no isolated hardware or backend causal comparison is established. Exact historical timer source: https://github.com/ggml-org/llama.cpp/blob/1425386fd996511e1f3295e7366c38289a92a271/tools/llama-bench/llama-bench.cpp . PP512 runs 512 synthetic random-token decode work units, then synchronizes. TG128 runs 128 synchronized single-token decode work units; these are not client-visible generated text tokens. Rates are per-trial (n_prompt+n_gen)/elapsed, then arithmetic mean and sample SD where supplied. Depth is prepared/restored before timing and excluded from both rate numerator and denominator. TTFT, client latency, request wall time and model-load time are not measured. No rate-derived TTFT proxy is supplied. The exact source defaults are batch2048, ubatch512, f16 K/V, five repetitions and warmup enabled; full per-run command lines are absent, so defaults are labeled source defaults rather than independently observed settings. bench.sh uses --no-mmap while the common README command omits it; load mode is left unknown rather than grafted from that recipe. Effective allocated context capacity is not reported. HF link https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507 is a canonical parent-model/architecture reference ONLY, verified public metadata revision 0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe. It is not the tested GGUF or evidence of that quant publisher; no reference-model bytes are claimed to match the benchmark. No weights downloaded. LlamaPerf numeric fields are rounded to the supported 0.1 tok/s step; exact source rates and standard deviations are retained above.