diff --git a/docs/Running Qwen3.6 27B FP8 on Dankclaw.md b/docs/Running Qwen3.6 27B FP8 on Dankclaw.md
index 515cab3..0f8c504 100644
--- a/docs/Running Qwen3.6 27B FP8 on Dankclaw.md	
+++ b/docs/Running Qwen3.6 27B FP8 on Dankclaw.md	
@@ -26,7 +26,7 @@ Qwen3.6-27B is a dense vision-language model from the Qwen3.6 series. It uses 27
 |----------|-------|
 | CPU | AMD RYZEN AI MAX+ 395 w/ Radeon 8060S (Strix Halo) |
 | GPU | Integrated Radeon 8060S (unified memory) |
-| Physical RAM | 30 GiB (shared CPU + GPU) |
+| Physical RAM | 64 GiB (61 GiB visible; corrected 2026-08-17) |
 | GPU-Visible (ROCm) | 96 GiB (overcommitted) |
 | Swap | 8 GiB |
 | ROCm | 7.2.4 |
@@ -43,7 +43,7 @@ Qwen3.6-27B is a dense vision-language model from the Qwen3.6 series. It uses 27
 | Overhead | ~2 GB | ~2 GB |
 | **Total (approximate)** | ~58 GB | ~16.5 GB |
 
-The FP8 variant requires approximately 16-17 GB. ThinkingCap-FP8 requires ~29 GB. Both fit within the 30 GiB physical RAM, but ROCm reports 96 GiB to vLLM. Set `--gpu-memory-utilization` conservatively (0.80) to avoid OOM.
+The FP8 variant requires approximately 16-17 GB. ThinkingCap-FP8 requires ~29 GB. Both fit within the 61 GiB physical RAM, but ROCm reports 96 GiB to vLLM. Set `--gpu-memory-utilization` conservatively (0.80) to avoid OOM.
 
 ## Prerequisites
 
diff --git a/docs/Shared Memory Architecture Dankclaw.md b/docs/Shared Memory Architecture Dankclaw.md
index 0142689..3fcbd37 100644
--- a/docs/Shared Memory Architecture Dankclaw.md	
+++ b/docs/Shared Memory Architecture Dankclaw.md	
@@ -142,7 +142,7 @@ Unified memory has lower bandwidth than dedicated VRAM:
 
 ### ROCm Overcommit
 
-ROCm reports 96 GiB GPU memory on Strix Halo APU. Physical RAM is 30 GiB. The GPU can address more than physical RAM exists. vLLM will see 96 GiB and try to allocate accordingly. Set `--gpu-memory-utilization` to 0.80 or lower to prevent OOM.
+ROCm reports 96 GiB GPU memory on Strix Halo APU. Physical RAM is 64 GiB (61 GiB visible; corrected 2026-08-17, the box was upgraded from 32 GB). The GPU can address more than physical RAM exists. vLLM will see 96 GiB and try to allocate accordingly. Set `--gpu-memory-utilization` to 0.80 or lower to prevent OOM.
 
 Lower bandwidth means slower inference. Plan for lower throughput.