AMD Ryzen AI 9 HX 370 (Strix Point)
Strix Point laptop SoC. XDNA 2 NPU at 50 TOPS INT8 + RDNA 3.5 iGPU. ROCm support on Linux unlocks llama.cpp ROCm path; on Windows, ONNX Runtime + DirectML.
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Sub-scores sum to 374 / 1000. Headline = 374 × 0.70 (Estimated-confidence discount) = 262. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 128 GB/s bandwidth — 12.8 tok/s estimated. No measured benchmarks yet.
Plain-English: Edge-of-fit for 7B; expect compromises.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
Our verdict
What it does well
The AMD Ryzen AI 9 HX 370 (Strix Point) is a top Ryzen AI 300 Series laptop APU and the most credible "AI laptop without a discrete GPU" pick on the Windows side. Twelve Zen 5 / Zen 5c CPU cores + 16-core RDNA 3.5 integrated GPU + dedicated XDNA 2 NPU rated at 50 TOPS — all in a laptop chassis. The unified memory architecture (typically 32 GB in laptops; AMD lists DDR5-5600 and LPDDR5x-8000) is shared across CPU + iGPU + NPU, which means smaller LLMs can use the full 32 GB DRAM ceiling without VRAM constraints. 7B–13B class weights fit in memory and run on the iGPU without a discrete GPU. Battery life under inference load is meaningfully better than RTX-equipped gaming laptops. The chip is excellent for "I want to run small AI models on my work laptop" segment without paying for a gaming-class discrete GPU.
Where it breaks
- No CUDA — RDNA 3.5 + XDNA 2 NPU are AMD ecosystems. llama.cpp ROCm + DirectML + ONNX Runtime work. On Linux, vLLM lists Ryzen AI 300 Series (gfx1150) on ROCm 7.0.2 or above, and SGLang documents a dedicated ROCm image for Strix Point (gfx1150). TensorRT-LLM does not run here. If your stack is CUDA-locked, this APU is friction.
- NPU framework support is thin. XDNA 2's 50 TOPS sounds compelling but real-world LLM throughput on the NPU is limited by software — most inference runs on the iGPU instead, where ROCm support is patchy on Windows.
- iGPU memory bandwidth limits decode speed. Shared LPDDR5x-8000 at 128 GB/s (arithmetic from AMD's memory speed) is dramatically below discrete GPU bandwidth (RTX 5090 Laptop GPU at 896 GB/s). For 13B+ workloads, decode is meaningfully slower than equivalent discrete-GPU laptops.
- Memory limits model size. AMD lists up to 256 GB, but at a typical 32 GB, OS + apps leave about 24 GB for LLM workloads. 70B Q4 doesn't fit. 32B FP16 doesn't fit. 14B Q4 fits with limited context.
- No real story for fine-tuning. Wrong tier — pick a discrete-GPU laptop or workstation.
- Variable system quality. The HX 370 ships in laptops with very different cooling + RAM configurations. Performance varies dramatically — read laptop reviews carefully before buying.
- Linux support is improving but laggy. Strix Point Linux drivers (AMD's Ryzen AI Linux instructions call for kernel 6.10 or newer) are functional but new-architecture kinks remain. Windows is the more polished path in 2026.
Ideal model range
- Sweet spot: 7B FP16 / Q5 inference on the iGPU, for IDE coding assistants and document Q&A.
- Sweet spot: 7B QLoRA inference for embedding models or specialized smaller-class fine-tunes.
- Sweet spot: Multi-model agentic loops fitting 24 GB total — 4B + embedding + small re-ranker.
- Sweet spot: Battery-life-friendly local AI for the traveling professional who doesn't need 24×7 fast inference.
- Stretch: 13B Q4 with 8K context.
- Bad fit: 32B-class anything, 70B-class anything, fine-tuning, production serving, anything that requires CUDA.
Bad use cases
- Anyone targeting 70B / 32B local AI. Hard memory ceiling + bandwidth ceiling. Pick a discrete-GPU laptop (Razer Blade 16, ASUS ROG Strix Scar 18) or MacBook Pro 16 M4 Max.
- CUDA-locked stacks. No CUDA. Don't pick AMD if the rest of your toolchain is NVIDIA.
- Production serving / sustained inference. Wrong tier — laptop APU.
- Maximum tok/s on small models. Even discrete laptop GPUs (RTX 4060/4070 Mobile) win decisively on bandwidth-bound decode.
- Heavy fine-tuning workflows. Pick a discrete GPU.
- Gaming + AI dual purpose. RDNA 3.5 iGPU is gaming-capable but a discrete RTX laptop is dramatically better at both AI and gaming.
Verdict
Buy this if you want a laptop that runs sub-13B local AI well (8B Q4 / Q5 at usable speed), you value battery life and silence over raw throughput, your stack is Windows-AMD-friendly (ROCm / DirectML / ONNX Runtime), and you don't need 14B+ models. AMD Ryzen AI 9 HX 370 is the right pick for the segment that wants "good enough" local AI on a normal-form-factor productivity laptop without paying for a gaming GPU.
Skip this if you need 14B+ models (jump to discrete GPU laptop), you're CUDA-locked (pick NVIDIA), you want maximum local AI performance (Razer Blade 16 with RTX 5090 Mobile is dramatically faster), you can use macOS (MacBook Pro M4 Max wins on memory ceiling + ecosystem maturity at higher tier), or you're production-serving (wrong category entirely).
How it compares
- vs Intel Core Ultra 7 258V (Lunar Lake) → Lunar Lake has Intel Arc Xe2 iGPU + 47 TOPS NPU vs Strix Point's RDNA 3.5 iGPU + 50 TOPS NPU. Intel lists 64 GPU TOPS (Int8) for the 258V; AMD lists 80 overall TOPS for the HX 370, 50 of them on the NPU. Both are sub-13B class machines. Pick by laptop OEM availability + Windows ecosystem preference.
- vs Razer Blade 16 (RTX 5090 Mobile) → Razer Blade 16 has 24 GB CUDA discrete GPU + dramatically more compute (32B Q4_K_M weights, about 19.4 GB, fit on the GPU; 70B Q4_K_M, about 42.4 GB, needs partial offload). Strix Point wins on battery life, silence, weight, sub-13B-class accessibility. Pick by workload size — sub-13B accept Strix Point, anything serious pick discrete GPU.
- vs MacBook Pro 16 M4 Max (128 GB unified) → MBP 16 wins on memory ceiling (4× the RAM), battery life, silence, ecosystem (MLX is more polished than ROCm-on-Windows). Strix Point laptops win on price, Windows ecosystem, AMD-aligned stacks. Pick by ecosystem and budget.
- vs Framework Laptop 16 (RX 7700S) → Framework Laptop 16 with discrete dGPU (8 GB RX 7700S) is similar AMD AI ecosystem at modest discrete GPU. Pick Framework for repairability + AMD discrete; HX 370 systems for newer NPU + Strix Point arch + better battery life on integrated-only workloads.
Overview
What the Ryzen AI 9 HX 370 actually is, in local-AI terms
The AMD Ryzen AI 9 HX 370 (Strix Point) is AMD's 2024-2025 Copilot+ PC laptop SoC. 12 Zen 5 / Zen 5c cores, an RDNA 3.5 integrated GPU, and the XDNA 2 NPU at 50 TOPS INT8 — the headline number that puts the chip past Microsoft's 40 TOPS Copilot+ certification floor.
For the local-AI operator looking at "what's the best AMD-powered AI laptop in 2026," the HX 370 is the answer. The same operator should also be honest about the trade: Strix Point is a meaningfully better laptop CPU + iGPU + NPU combination than the Hawk Point predecessor, but it does not change the fundamental fact that on-device AI on x86 laptops in 2026 is a 7B-class story, not a 32B-class story.
Where it fits in the hardware ladder
In the Copilot+ x86 laptop tier:
| Chip | NPU TOPS | iGPU | Mem BW | Notes |
|---|---|---|---|---|
| Intel Lunar Lake (258V) | 47 | Xe2 | 136 GB/s | Intel Copilot+ chip |
| AMD Ryzen AI 9 HX 370 | 50 | RDNA 3.5 | 128 GB/s | AMD Copilot+ chip |
| AMD Ryzen AI 9 365 | 50 | RDNA 3.5 | 128 GB/s | sibling chip, slightly fewer cores |
| Snapdragon X Elite | 45 | Adreno X1 | 135 GB/s | ARM Copilot+ alternative |
vs the Apple alternative: the Apple M4 Max plays in a different league for "real LLM inference" because of memory bandwidth (~400+ GB/s) and unified-memory capacity. The HX 370 is competitive for NPU-accelerated small-model inference, not for transformer attention bound on memory bandwidth.
Best use cases
- Copilot+ native Windows AI workloads. Phi-4 / Llama 3.2 1B / 3B running through OnnxRuntime GenAI (OGA) on the NPU. Native Windows on-device AI is what Strix Point is built for.
- Linux laptop with a usable iGPU LLM path. ROCm support on Strix Point's RDNA 3.5 iGPU is real in 2026 and lets you run llama.cpp GPU-accelerated on a laptop without a dGPU.
- Battery-aware on-device coding assistant. Small coding models (Qwen 2.5 Coder 1.5B / 3B) routed through the NPU keep the dGPU idle, save battery.
- Enterprise compliance laptops. Air-gapped on-device AI for fields where cloud inference is prohibited.
- Developer dev box with light local-AI workloads. Use the laptop for prototyping, do real inference on a workstation.
For the laptop pattern see /stacks/offline-rag-workstation.
What it can run
The story is memory-bandwidth-bound, not compute-bound. Peak system DRAM bandwidth (128 GB/s on LPDDR5x-8000, 89.6 GB/s on DDR5-5600, by arithmetic) is the actual ceiling on transformer decode tok/s.
| Model class | Quant | Path | Realistic tok/s |
|---|---|---|---|
| 1B-3B | INT4 / INT8 | NPU + OnnxRuntime GenAI | usable, snappy |
| 7B-8B | Q4_K_M | iGPU via ROCm + llama.cpp | usable for short prompts |
| 7B-8B | quantized ONNX | NPU via OnnxRuntime GenAI | usable |
| 13B | Q4_K_M | iGPU + 32 GB RAM | works but slow |
| 32B+ | — | — | unrealistic on a laptop |
The headline 50 TOPS INT8 number is meaningful for prefill but transformer decode is bandwidth-bound, and memory bandwidth is the wall.
OS support
| OS | Quality | Notes |
|---|---|---|
| Windows 11 (24H2+) | excellent | the Copilot+ path; OnnxRuntime GenAI for LLMs and ONNX Runtime + Vitis AI EP for other models on the NPU, DirectML on the GPU |
| Linux (Ubuntu 24.04 LTS) | good | AMD's ROCm matrix lists the Radeon 890M (gfx1150) on Ubuntu 24.04.5 with the HWE kernel; some laptop quirks |
| Linux (other) | partial | distro-dependent driver packaging |
| WSL2 | partial | GPU passthrough exists; rougher than native |
| macOS | unsupported |
If your day job is Linux dev, expect a few weeks of debugging to get full ROCm + NPU + power management working cleanly.
Software / runtime support
- OnnxRuntime GenAI (OGA): the LLM path on the NPU in AMD's Ryzen AI docs; ONNX Runtime + Vitis AI EP covers CNN and transformer models, and DirectML is AMD's documented GPU flow
- AMD Ryzen AI Software (XDNA driver): the official NPU access SDK, on Windows 11 and, from Strix Point onward, Ubuntu 24.04
- ROCm on Linux — works on the RDNA 3.5 iGPU with the right gfx target
- llama.cpp HIP — works on Linux; the Linux-native path
- llama.cpp Vulkan — cross-platform fallback; usable on Windows when ROCm-on-iGPU isn't an option
- Ollama — works on Linux via HIP, on Windows via Vulkan
- OpenVINO — partial support; primarily an Intel path
- CUDA / TensorRT-LLM / ExLlamaV2 — wrong vendor
For format support across runtimes see /systems/quantization-formats.
What breaks first
- NPU access on non-Windows. XDNA Linux driver work is ongoing in 2026 but Windows is dramatically smoother for NPU paths.
- iGPU memory budget. The iGPU shares system DRAM; allocating 16 GB to an LLM leaves less for the OS / apps. Plan around 32 GB minimum, ideally 64 GB for serious work.
- Power management vs throughput. Sustained inference pushes the chip to its configured power limit (28 W default, 15-54 W configurable per AMD); battery life collapses. Plug in for serious workloads.
- Driver lineage drift on Linux. ROCm + amdgpu + linux-firmware versions need to match; mismatches surface as silent CPU fallback. See /errors/rocm-device-not-found.
- Bleeding-edge model architectures on the NPU. ONNX-conversion-and-NPU-deployment workflow assumes the model converts cleanly; novel architectures often need manual op-implementations.
Alternatives by intent
| If you want… | Reach for |
|---|---|
| Intel x86 Copilot+ chip | Intel Lunar Lake (258V) |
| ARM Windows alternative | Snapdragon X Elite |
| Apple-ecosystem on-device | Apple M4 Max |
| Real serious LLM workstation | RTX 4070 Ti Super or RTX 4090 desktop |
| Older AMD AI laptop (cheaper) | Hawk Point Ryzen AI 8040 |
| Maximum unified memory on Apple | Apple M3 Ultra Mac Studio |
Best pairings
- Windows 11 24H2 + OnnxRuntime GenAI + Phi-4 / Llama 3.2 — the Copilot+ canonical setup
- Ubuntu 24.04 LTS + ROCm + llama.cpp HIP — the Linux dev box default
- Ollama + Llama 3.1 8B Q4_K_M — the cross-platform homelab-on-laptop fallback
- 64 GB system RAM — non-negotiable for serious laptop AI; iGPU borrows from main DRAM
- Plugged-in operation for LLM workloads — battery-only is fine for 1-3B models, painful for 8B+
Who should avoid the Ryzen AI 9 HX 370
- Anyone expecting workstation-class throughput from a laptop. Wrong tier.
- Operators on a CUDA-only software stack. Wrong vendor.
- Multi-user-serving production. Wrong form factor.
- Apple-ecosystem operators. Stay with Apple Silicon.
- Linux purists who want zero-friction. The Strix Point Linux experience is good but not the smoothest path; M4 Max + macOS is smoother for "just works."
- Workloads that need 24+ GB of GPU memory. Wrong tier; buy a desktop dGPU.
Related
- Stacks: /stacks/offline-rag-workstation, /stacks/android-on-device-ai
- System guides: /systems/linux-local-ai, /systems/quantization-formats
- Tools: ONNX Runtime, OpenVINO, llama.cpp, ROCm
- Hardware: Intel Lunar Lake (258V), Snapdragon X Elite, Apple M4 Max
- Errors: /errors/rocm-device-not-found, /errors/wsl2-gpu-not-detected
Prices quoted in the review text are estimates from when it was written. Check the current price before you buy.
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Specs
| VRAM | 0 GB |
| System RAM (typical) | 32 GB |
| Power draw (peak) | 28 W |
| Released | 2024 |
| Backends | ROCm Vulkan |
Hardware worth comparing
The closest alternatives by price, memory bandwidth, and form factor, plus a step up and down — so you can frame the buying decision against real options.
Ryzen AI 9 HX 370 (Strix Point) is the headline integrated-graphics AI part of 2026. The iGPU + eGPU guides below frame this decision space.
Frequently asked
Does AMD Ryzen AI 9 HX 370 (Strix Point) support CUDA?
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Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.