diff --git a/skills/ms-ai-security/references/performance-scalability/gpu-compute-sizing.md b/skills/ms-ai-security/references/performance-scalability/gpu-compute-sizing.md
index 683e9c7..f960ec3 100644
--- a/skills/ms-ai-security/references/performance-scalability/gpu-compute-sizing.md
+++ b/skills/ms-ai-security/references/performance-scalability/gpu-compute-sizing.md
@@ -1,6 +1,6 @@
 # GPU and Compute Sizing for AI
 
-**Last updated:** 2026-04 | Verified: MCP 2026-04
+**Last updated:** 2026-06-24 | Verified: MCP 2026-06
 **Status:** GA
 **Category:** Performance & Scalability
 
@@ -34,7 +34,7 @@ For norsk offentlig sektor er GPU-dimensjonering relevant ved deployment av open
 | NC4as_T4_v3 | 1x NVIDIA T4 | 16 GB | Liten modell-inferens | Lavest |
 | NC24ads_A100_v4 | 1x NVIDIA A100 | 80 GB | Medium modell-inferens/trening | Middels |
 | NC96ads_A100_v4 | 4x NVIDIA A100 | 320 GB | Stor modell-trening | Høy |
-| ND96asr_v4 | 8x NVIDIA A100 | 640 GB | LLM-trening, multi-GPU | Svært høy |
+| ND96asr_v4 | 8x NVIDIA A100 (40 GB) | 320 GB | LLM-trening, multi-GPU | Svært høy |
 | ND96isr_H100_v5 | 8x NVIDIA H100 | 640 GB | Frontier-modell trening | Høyest |
 | NC40ads_H100_v5 | 1x NVIDIA H100 | 80 GB | Stor modell-inferens | Høy |