Technical Intelligence Analyst Report - 2026-02-13

Executive Summary

  • EPYC 9005 “Turin” Confidential Computing: New benchmarking analysis evaluates the performance overhead of AMD SEV-SNP (Secure Encrypted Virtualization with Secure Nested Paging) on the latest EPYC 9005 processors within Microsoft Azure.
  • Security vs. Performance: The report highlights the trade-offs involved in enabling hardware-backed security, generally citing a 2-10% performance cost, with higher impacts on I/O-heavy workloads.
  • Software Ecosystem: Testing includes upcoming software stacks, specifically Ubuntu 26.04 development snapshots with GCC 15, indicating readiness for next-generation Linux enterprise deployments.

🔲 AMD Hardware & Products

[2026-02-13] Evaluating The Performance Cost To AMD SEV-SNP On EPYC 9005 VMs

Source: Phoronix

Key takeaway relevant to AMD:

  • Validates the viability of EPYC 9005 “Turin” processors for confidential computing in major cloud environments (Azure).
  • Provides critical data for enterprise customers weighing the performance penalty of enabling SEV-SNP against the security benefits.
  • Demonstrates forward-looking compatibility with upcoming Linux distributions (Ubuntu 26.04) and compiler stacks (GCC 15).

Summary:

  • Phoronix conducted a performance analysis of AMD SEV-SNP on EPYC 9005 “Turin” servers using Microsoft Azure.
  • The comparison focused on the performance delta between a standard VM and a Confidential VM (CVM) with SEV-SNP enabled.
  • Testing utilized both current Ubuntu 24.04 LTS and an early development snapshot of Ubuntu 26.04.

Details:

  • Technology Scope: The review focuses on AMD SEV-SNP (Secure Encrypted Virtualization with Secure Nested Paging). This feature provides memory encryption, integrity protections, and defenses against malicious hypervisor-based attacks and side-channel exploits.
  • Performance Expectations:
    • The article notes a typical reported performance overhead of 2% to 10% when engaging SEV-SNP.
    • I/O heavy workloads (e.g., database servers) may experience higher overheads, cited between 10% and 12%.
  • Hardware Configuration:
    • Platform: Microsoft Azure v7 series VMs.
    • Processor: AMD EPYC 9V74 (80-core processors).
    • Test Instance: A 16 vCPU configuration (8 physical cores + 16 threads), 64GB RAM, and 550GB virtual storage.
    • New Feature Note: EPYC 9005 supports SEV Trusted I/O for PCIe device protection, though this specific feature was outside the scope of this test.
  • Software Environment:
    • Baseline: Ubuntu 24.04 LTS running Linux kernel 6.14 and GCC 13.2.
    • Forward-Looking: Ubuntu 26.04 development snapshot tested to evaluate the impact of newer kernels and the GCC 15 compiler.
  • Methodology: A direct 1:1 comparison was made between a non-confidential instance and a SEV-SNP enabled instance to isolate the performance cost of the security features.

📈 GitHub Stats

Category Repository Total Stars 1-Day 7-Day 30-Day
AMD Ecosystem AMD-AGI/GEAK-agent 63 0 +2  
AMD Ecosystem AMD-AGI/Primus 74 0 +1  
AMD Ecosystem AMD-AGI/TraceLens 58 0 0  
AMD Ecosystem ROCm/MAD 31 0 0  
AMD Ecosystem ROCm/ROCm 6,169 +2 +25  
Compilers openxla/xla 3,983 +2 +13  
Compilers tile-ai/tilelang 5,177 +12 +109  
Compilers triton-lang/triton 18,408 +1 +45  
Google / JAX AI-Hypercomputer/JetStream 407 0 +3  
Google / JAX AI-Hypercomputer/maxtext 2,138 -2 +6  
Google / JAX jax-ml/jax 34,854 +5 +50  
HuggingFace huggingface/transformers 156,438 +26 +283  
Inference Serving alibaba/rtp-llm 1,049 +1 +8  
Inference Serving efeslab/Atom 336 0 0  
Inference Serving llm-d/llm-d 2,485 +3 +32  
Inference Serving sgl-project/sglang 23,494 -43 +96  
Inference Serving vllm-project/vllm 70,229 +78 +582  
Inference Serving xdit-project/xDiT 2,539 +2 +13  
NVIDIA NVIDIA/Megatron-LM 15,206 +3 +57  
NVIDIA NVIDIA/TransformerEngine 3,160 0 +18  
NVIDIA NVIDIA/apex 8,915 0 +4  
Optimization deepseek-ai/DeepEP 8,976 -4 +11  
Optimization deepspeedai/DeepSpeed 41,613 +2 +62  
Optimization facebookresearch/xformers 10,336 +1 +10  
PyTorch & Meta meta-pytorch/monarch 967 -1 +9  
PyTorch & Meta meta-pytorch/torchcomms 331 -1 +3  
PyTorch & Meta meta-pytorch/torchforge 620 +4 +7  
PyTorch & Meta pytorch/FBGEMM 1,530 0 +4  
PyTorch & Meta pytorch/ao 2,685 +6 +18  
PyTorch & Meta pytorch/audio 2,826 -1 +4  
PyTorch & Meta pytorch/pytorch 97,382 +21 +182  
PyTorch & Meta pytorch/torchtitan 5,066 +3 +25  
PyTorch & Meta pytorch/vision 17,507 -4 +10  
RL & Post-Training THUDM/slime 4,033 +118 +336  
RL & Post-Training radixark/miles 874 +1 +29  
RL & Post-Training volcengine/verl 19,194 +17 +159