Executive Summary

  • The upcoming Linux 7.1 kernel brings critical telemetry and observability features to AMD’s Ryzen AI NPUs via the AMDXDNA accelerator driver.
  • Developers will now have access to real-time power estimates and hardware utilization metrics directly from user-space, closing a major tooling gap for mobile and desktop AI development on AMD platforms.
  • These driver enhancements are launching alongside new inference software (Lemonade 100 and FastFlowLM 0.9.35), signaling a maturing Linux ecosystem for local LLM execution on AMD hardware.

🤖 ROCm Updates & Software

[2026-03-14] Linux 7.1 Will Bring Power Estimate Reporting For AMD Ryzen AI NPUs

Source: Phoronix (AMD Linux)

Key takeaway relevant to AMD:

  • AMD is significantly improving observability and profiling capabilities for Ryzen AI NPUs in Linux environments.
  • By exposing power and utilization metrics, AMD enables developers to accurately profile, benchmark, and optimize local AI workloads (like LLMs) for better performance-per-watt on Ryzen processors.

Summary:

  • Recent drm-misc-next patches destined for the Linux 7.1 kernel introduce significant updates to the AMDXDNA accelerator driver.
  • The patches allow user-space applications to read real-time power estimates and column utilization (busyness) metrics from Ryzen AI NPUs.

Details:

  • Kernel Version: Features are included in the drm-misc-next pull request, targeting the Linux 7.1 kernel release.
  • Driver Infrastructure: Modifies the AMDXDNA accelerator driver in conjunction with the AMD PMF platform driver.
  • Power Telemetry: Introduces a new ioctl specifically for reading real-time NPU hardware power estimates. This is exposed to user-space via the DRM_IOCTL_AMDXDNA_GET_INFO command.
  • Utilization Metrics: Adds support for real-time “column utilization” tracking, providing a highly granular metric to determine exactly how saturated/busy the NPU hardware is at any given moment.
  • Ecosystem Integration: The article notes that these hardware observability features arrive right as Ryzen AI NPUs become highly viable for Linux-based LLM execution, specifically supporting the newly released “Lemonade 100” and “FastFlowLM 0.9.35” frameworks.
  • Implications for Developers/Users: Previously, developers operating AI models on AMD NPUs under Linux had limited visibility into hardware efficiency. These additions allow engineers to gauge power consumption versus inference speed, facilitating the development of highly optimized, battery-friendly local AI applications.

📈 GitHub Stats

Category Repository Total Stars 1-Day 7-Day 30-Day
AMD Ecosystem AMD-AGI/GEAK-agent 73 0 +4 +10
AMD Ecosystem AMD-AGI/Primus 82 0 +6 +8
AMD Ecosystem AMD-AGI/TraceLens 63 0 0 +5
AMD Ecosystem ROCm/MAD 31 0 0 0
AMD Ecosystem ROCm/ROCm 6,247 0 +22 +80
Compilers openxla/xla 4,069 +3 +20 +88
Compilers tile-ai/tilelang 5,365 +1 +36 +200
Compilers triton-lang/triton 18,656 +13 +84 +249
Google / JAX AI-Hypercomputer/JetStream 415 0 0 +8
Google / JAX AI-Hypercomputer/maxtext 2,169 0 +7 +29
Google / JAX jax-ml/jax 35,080 +4 +65 +231
HuggingFace huggingface/transformers 157,795 +27 +285 +1383
Inference Serving alibaba/rtp-llm 1,066 0 +9 +18
Inference Serving efeslab/Atom 335 0 -1 -1
Inference Serving llm-d/llm-d 2,614 +5 +29 +132
Inference Serving sgl-project/sglang 24,440 +21 +239 +903
Inference Serving vllm-project/vllm 73,059 +63 +741 +2908
Inference Serving xdit-project/xDiT 2,566 0 +6 +29
NVIDIA NVIDIA/Megatron-LM 15,647 +7 +113 +444
NVIDIA NVIDIA/TransformerEngine 3,210 +3 +23 +50
NVIDIA NVIDIA/apex 8,931 +1 +3 +16
Optimization deepseek-ai/DeepEP 9,044 0 +21 +64
Optimization deepspeedai/DeepSpeed 41,807 +4 +49 +196
Optimization facebookresearch/xformers 10,369 +2 +7 +34
PyTorch & Meta meta-pytorch/monarch 989 0 +4 +21
PyTorch & Meta meta-pytorch/torchcomms 347 0 +2 +15
PyTorch & Meta meta-pytorch/torchforge 641 0 +9 +25
PyTorch & Meta pytorch/FBGEMM 1,540 0 +4 +10
PyTorch & Meta pytorch/ao 2,730 0 +9 +51
PyTorch & Meta pytorch/audio 2,841 +2 +7 +14
PyTorch & Meta pytorch/pytorch 98,235 +29 +217 +874
PyTorch & Meta pytorch/torchtitan 5,141 +2 +30 +78
PyTorch & Meta pytorch/vision 17,565 +1 +19 +54
RL & Post-Training THUDM/slime 4,754 +14 +149 +839
RL & Post-Training radixark/miles 973 -1 +17 +100
RL & Post-Training volcengine/verl 19,892 +14 +198 +715