High-intent questions about the first-generation AMD Ryzen AI NPU (XDNA1 / Phoenix / Hawk Point) on Linux, answered honestly. If you found this by searching an error string, jump to Troubleshooting.
Partially, and honestly: there is no turnkey LLM runtime for XDNA1 on Linux (FastFlowLM
and AMD Lemonade are XDNA2-only — Strix/Krackan). But the NPU itself is fully usable from
open source: open-xdna brings up the gen-1 NPU with AMD's open XRT + XDNA driver + IRON/
mlir-aie, and demonstrates real compute on the silicon — a verified 512³ matmul (~68 GFLOPS),
a 2-layer MLP forward pass, and a non-bijunctive prune/collapse kernel. A full model does not
yet run end-to-end on the NPU (no XDNA ggml backend exists).
Yes. The Phoenix/Hawk-Point NPU enumerates (lspci -d 1022:1502 → "AMD IPU Device"), the
in-kernel amdxdna driver (Linux 6.14+) creates /dev/accel/accel0, and with the userspace
from this repo it runs kernels. Verified on Ubuntu 25.10 / kernel 6.17 / GCC 15.
No. Measured here: the NPU is ~6× slower than the 780M at dense matmul and uses more energy per GFLOP for dense work. Its real value is low absolute power (~6.6 W vs the iGPU's ~26 W) and doing cheap selection/pruning that lets the iGPU do less work. Use the iGPU (Vulkan) for the matmuls; consider the NPU for low-power always-on or prune/collapse steps.
It's the open-source bring-up and experimentation path for the generation those tools skip.
It is not a drop-in ollama-style server. If you have a Phoenix/Hawk-Point chip and want the
NPU usable on Linux at all, this is the starting point.
Sparse, local, data-dependent work — not big dense GEMM. Confirmed on silicon: reductions
(reduce_max), elementwise activations (relu), and non-bijunctive top-k collapse
(keep-strong / prune-weak). The AIE2 vector ISA (aie::load_v, aie::mul, aie::add,
aie::max, aie::select) is the same primitive class as PowerPC AltiVec / VSX, so custom
kernels are hand-authorable in C++ intrinsics and compiled with Peano.
Ryzen 7 7840HS/U, 8840HS/U, 8845HS; Ryzen 9 7940HS, 8945HS; Ryzen 5 7640HS/U, 8640HS, 8645HS; desktop 8600G / 8700G. The desktop 8500G / 8300G have NO NPU (Zen4c die).
| Symptom | Cause / Fix |
|---|---|
xrt-smi examine → 0 devices found |
XRT needs libxrt_driver_xdna.so on its lib path (not just libvxdna.so). See docs/BRINGUP.md fix #1. |
Permission denied on /dev/accel/accel0 |
Run as root, or join the render group and re-login. |
aie2_query_telemetry/GET_ARRAY Operation not supported / -EINVAL |
Mainline amdxdna.ko (≤6.17) lacks ioctls the current SHIM needs — load the staging driver. Fix #2. |
objcopy: Unable to recognise the format of an AIE .o |
GNU objcopy can't parse AIE2 ELF; put llvm-objcopy on PATH (/usr/lib/llvm-XX/bin). Fix #3. |
ERT_CMD_STATE_ABORT / xdna_mailbox ret -22 / "Command bo too large" |
NPU firmware too old — install the version the runtime expects (e.g. npu.sbin.1.5.5.391). Fix #4. |
Host test link error __cxa_call_terminate@CXXABI_1.3.15 |
C++ ABI mismatch — build host with CC=gcc-15 CXX=g++-15 (match XRT's GCC), or run the pure-JIT .py path. |
aie.dma_bd Size exceeds [1:64] / allocated buffers exceeded available memory |
Matmul shape too large for the demo kernel — block-tile host-side into NPU-native (~512³) tiles. |
README.md (overview) → docs/BRINGUP.md (full recipe) → RESULTS.md (measured numbers) →
examples/ (runnable: npu_tiny_mlp.py, npu_collapse.py).