Can AMD GPUs render Blender Cycles in 2025?

Yes. Cycles supports AMD via HIP on Windows and Linux for RDNA1+ GPUs (RX 5000/6000/7000/9000 and Radeon Pro W-series). Keep drivers within Blender’s documented minimums.

Does Redshift work on AMD GPUs?

Yes on Windows via HIP. Match Maxon’s recommended AMD Pro driver revision and HIP compiler version in the Redshift release notes.

Can I operate a Render Network (RNDR) node with AMD?

RNDR nodes are still effectively NVIDIA/CUDA on Windows/Linux. AMD nodes are better monetized for Cycles/Redshift work outside RNDR.

Is AMD viable for ML workloads?

Inference on Linux via ONNX Runtime ROCm is solid. PyTorch ROCm works for training with careful stack pinning. Windows has HIP SDK and DirectML but Linux remains the more mature target for ML.

AMD on DePIN (2025): The Real State of ROCm & HIP Rendering vs. ML Compatibility

Can AMD GPUs earn on decentralized GPU networks today? Short answer: yes for a growing chunk of rendering, and limited, but improving options for ML. This operator-focused guide explains what actually works in 2025 across ROCm/HIP on Linux and Windows, which DCCs and frameworks run well, how DePIN networks treat AMD hardware, and concrete setups you can deploy without burning time.

2025 snapshot: Where AMD stands on DePIN

Rendering (good-to-strong fit)

Blender Cycles on AMD works via HIP on Windows and Linux (RDNA1+). Ray tracing HIP-RT continues to mature. Great for stills/animations when pipelines target Cycles.
Redshift supports AMD via HIP on Windows (and select workflows). It’s production-viable for many studios; driver/HIP versions matter.
OctaneRender still requires CUDA (NVIDIA) on Windows/Linux. Octane X lives on Apple Metal; practical AMD options here are limited if you operate PC nodes.

Machine Learning (limited, improving)

Linux: PyTorch ROCm wheels exist; ONNX Runtime ROCm is solid for inference on supported cards. Training is doable but requires careful stack pinning.
Windows: HIP SDK brings a subset of ROCm; ML stacks lean on DirectML or ONNX Runtime (DirectML/ROCm). For many ML DePINs today, NVIDIA is still the default.
Framework maturity is good enough for inference, mixed for large-model training with complex kernels.

Bottom line for operators: If you’re targeting rendering workloads (Blender/Redshift), AMD GPUs are increasingly viable and cost-effective. If you’re targeting ML inference, AMD is workable on your own infra or Akash-style clouds; but the big DePIN AI marketplaces today largely remain NVIDIA-first.

ROCm & HIP in plain English (and what changed in 2025)

ROCm is AMD’s open compute stack for running GPU-accelerated workloads on Linux (with an expanding Windows story through the HIP SDK). HIP is the C++ portability layer (“CUDA-like”) that underpins many app integrations (Blender Cycles, Redshift, PyTorch ROCm, ONNX Runtime ROCm).

Key moving pieces

ROCm on Linux: full stack (drivers, compiler, libraries like MIOpen, RCCL, MIGraphX).
HIP SDK on Windows: subset of ROCm + AMD Pro driver path; enough for rendering apps and some ML workflows, still behind Linux for bleeding-edge training stacks.
Framework wheels: Vendors ship ROCm-targeted builds (PyTorch, ONNX Runtime). Version pinning is critical.

Hardware scope in 2025

CDNA (Instinct MI) for data center training/inference.
RDNA (Radeon RX/Pro) for workstations and creators (5000/6000/7000/9000 series).
Compatibility is matrix-driven: OS, kernel, GPU architecture, and specific framework versions matter.

What’s new in 2025: Latest ROCm releases add broader distro support, newer RDNA architectures, and updated framework combos (e.g., PyTorch 2.5–2.7, ONNX Runtime 1.22+). On Windows, the HIP SDK matures with clearer GPU/driver lists and known-issues pages; still not “feature-parity” with Linux, but increasingly usable for creators and some inference roles.

Rendering on AMD: Blender Cycles & Redshift (yes), Octane (no on Windows/Linux)

Blender Cycles (HIP)

Blender’s Cycles uses HIP to run on AMD GPUs, with official support on Windows and Linux for RDNA1 and newer (RX 5000/6000/7000/9000; Radeon Pro W-series). Vega support was removed in recent releases. For a DePIN operator, this means a modern Radeon workstation or farm node renders Cycles natively without CUDA.

Set once, forget: In Blender → Edit → Preferences → System: choose HIP and check your AMD card(s). Keep Blender and drivers aligned with the supported versions (e.g., Windows Pro driver revs noted in Blender docs; ROCm version on Linux) to avoid “No compatible GPU” messages.

Redshift (HIP on Windows)

Redshift officially supports AMD GPUs on Windows via HIP, with specific driver/HIP compiler requirements. Many studios report stable use on W7900/W7800 and RX 7900-class cards, provided they install the recommended AMD Software: Pro Edition driver aligned to Redshift’s HIP compiler version. Expect significant gains over CPU-only modes and “good enough” parity for many production scenes, though NVIDIA-specific features (OptiX-only paths) are of course not applicable.

Operator checklist (Redshift on AMD)

Install AMD Software: Pro Edition (the exact revision Redshift recommends).
Install the HIP SDK/runtime combo if your workflow requires it (some installers bundle). Match Redshift’s HIP compiler version notes.
Test a known scene on CPU backend, then GPU (HIP) backend; verify no denoiser/OSL surprises.
Pin versions across your farm (driver + Redshift + DCC). Avoid mixing driver branches.

Cards that work well

Radeon Pro W-series (W7900/W7800) for large scenes, VRAM headroom.
RX 7900 XTX/XT for cost-efficient single-box nodes.
Mixing consumer + Pro is fine if drivers/HIP are compatible and your scenes fit VRAM.

OctaneRender (CUDA on Windows/Linux; Metal on macOS)

OctaneRender remains CUDA-only on Windows/Linux. On macOS, Octane X targets Apple Silicon via Metal; historic AMD dGPU support on older macOS builds has narrowed. Practically speaking, if a rendering pipeline or DePIN marketplace requires Octane on PC nodes, you’ll need NVIDIA. If your marketplace accepts Blender Cycles or Redshift jobs, AMD nodes are viable.

Takeaway: For decentralized rendering, choose networks or clients aligned to Blender Cycles or Redshift HIP if you want AMD nodes to earn. Octane-exclusive pipelines still lock you into NVIDIA on Windows/Linux.

ML on AMD: PyTorch/ONNX on Linux, DirectML/HIP SDK on Windows

On Linux, PyTorch ROCm and ONNX Runtime ROCm are the default way to run AMD GPUs for training/inference. On Windows, you have two broad paths:
(a) HIP SDK + AMD Pro drivers for HIP-enabled apps and some frameworks, and
(b) DirectML (PyTorch-DirectML / TensorFlow-DirectML) for universal Windows GPU acceleration, including AMD. For operators, the main friction is consistency: you must pin ROCm/HIP and framework versions tightly and avoid drifting drivers mid-project.

Linux (preferred for ML)

Use supported distros (Ubuntu 22.04/24.04; RHEL/Rocky; SLES) per the ROCm compatibility matrix.
Install ROCm, then install pytorch-rocm wheel(s) or ONNX Runtime ROCm per vendors’ docs.
For multi-GPU, RCCL replaces NCCL; be mindful of topology and PCIe/BAR sizing.

Windows (growing, still maturing)

HIP SDK brings a subset of ROCm; confirm your GPU is listed in AMD’s Windows support table.
DirectML works with PyTorch/TensorFlow on a wide range of DX12 GPUs, but kernel performance can vary by model class.
ONNX Runtime supports DirectML EP on Windows and ROCm EP on Linux/WSL for inference pipelines.

Practical ML use cases on AMD in 2025

Stable Diffusion / image pipelines: ONNX Runtime ROCm or PyTorch ROCm on Linux is straightforward and efficient; DirectML works on Windows for hobbyist to semi-pro workflows with careful VRAM budgeting.
LLM inference: ONNX Runtime ROCm or llama.cpp GPU backends that target AMD (via HIP) can be smooth. For quantized models, ensure kernels map to HIP/MIopen paths efficiently.
Training: Small/medium models are viable; large-scale training still demands deep stack tuning. If your goal is DePIN monetization for AI, remember many marketplaces today match clients to NVIDIA first.

Version-pinning rule of thumb: Freeze (1) ROCm/HIP version, (2) driver revision, (3) framework version, and (4) container base image. Update only when your target workloads require it.

DePIN networks: where can AMD earn today?

Rendering-centric

Render Network (RNDR): Historically CUDA/Octane-centric for node operators, which de-facto means NVIDIA on Windows/Linux. Even though Blender Cycles support exists in RNDR tooling, node hardware requirements remain NVIDIA-leaning; operating AMD nodes here is not practical today.
Freelance/agency pipelines (off-chain): If your clients render in Blender Cycles or Redshift (HIP), AMD nodes are easy to monetize with conventional queues, containers, or farm managers—no tokenization required.

AI-centric

io.net: Operator docs emphasize NVIDIA configuration (nvidia-smi, CUDA containers). AMD/ROCm is not a general-availability path for workers as of 2025.
Nosana: Host docs list NVIDIA GPU families and markets; AMD GPUs are not currently listed as compatible hosts.
Akash (decentralized cloud): You can advertise AMD nodes and rent them out for general compute; viability depends on client demand and your ability to supply containers with ROCm/ONNX/PyTorch pre-baked.

Recommendation by goal:

Rendering revenues now: AMD nodes aimed at Blender Cycles or Redshift clients. Consider private queues, agency partnerships, or marketplaces that accept Cycles/Redshift jobs.
AI inference side-income: Package ONNX Runtime ROCm images on Linux; advertise on Akash or direct B2B. Keep expectations conservative vs. NVIDIA-centric DePINs.

Operator blueprints: stable AMD setups (Linux & Windows)

A. Rendering box (Windows • Redshift/Blender)

Hardware

CPU: 12–24 cores (scene prep is CPU-bound).
GPU: Radeon Pro W7900/W7800 or RX 7900 XTX (24GB VRAM sweet spot for many jobs).
RAM: 64–128GB; Storage: NVMe 2TB+.
Cooling: front-to-back airflow; render temps steady <80°C.

Software

AMD Software: Pro Edition (per Redshift’s recommended rev).
Redshift latest (HIP compiler per release notes); Blender LTS (HIP enabled).
Farm manager: Deadline, Royal Render, or simple queue scripts + watch folders.

Validation steps: run a known Cycles benchmark + a Redshift production scene; confirm denoiser/OSL nodes; check VRAM headroom; log render times; store a golden CSV for future comparisons when drivers change.

B. ML inference node (Linux • ONNX Runtime ROCm)

Hardware

GPU: RX 7900 XTX (24GB) or Pro W7900 (48GB) for larger batch sizes.
CPU: modest (8–16 cores), 64–128GB RAM, NVMe scratch for model cache.
NIC: 2.5/10GbE if serving many concurrent requests.

Software

Supported Linux distro (per ROCm matrix); ROCm installer.
ONNX Runtime ROCm (pip wheel pinned per docs); optional Triton/Olive as needed.
Serving: FastAPI or OpenAI-compatible shim; Prometheus + Loki for metrics/logs.

# Example (Linux) — create ROCm + ORT venv
sudo usermod -aG render,video $USER && newgrp render
python3 -m venv ~/venvs/rocm-onnx && source ~/venvs/rocm-onnx/bin/activate
pip install --upgrade pip
pip install onnxruntime-rocm==<pin_per_docs> numpy==1.26.4  # match doc notes
pip install fastapi uvicorn pydantic

# Simple inference stub (CPU fallback if ROCm missing)
python -c "import onnxruntime as ort; print(ort.get_available_providers())"

C. Windows inference workstation (DirectML or HIP SDK apps)

When clients require Windows (or you’re dual-using the rig for DCC apps), you can:

Use PyTorch-DirectML for generic acceleration of supported models.
Use ONNX Runtime with DirectML EP for broad model coverage.
Run HIP-enabled apps (e.g., Blender/Redshift) natively for rendering on the same box.

# Example (Windows PowerShell) — DirectML env for PyTorch
py -m venv C:\venvs\dml && C:\venvs\dml\Scripts\activate
pip install --upgrade pip
pip install torch-directml
python -c "import torch_directml as dml; print('DML device ok')"

Tip: If serving external traffic, put Windows behind an NGINX reverse proxy (or Cloudflare Tunnel) that forwards to your app port. Keep the render queue and ML inference processes isolated via services or containers.

Performance & tuning: squeezing AMD for 24×7 workloads

Rendering tips

VRAM headroom: Render nodes crash when VRAM tops out. Keep 10–20% free; use out-of-core features sparingly; pack textures.
HIP/driver pinning: Change one variable at a time (driver → test; DCC update → test). Record bench deltas.
Scene prep: Bake modifiers, trim texture sets, decimate background assets. AMD vs. NVIDIA is often decided by scene discipline, not marketing slides.
HIP-RT: On Linux, HIP-RT keeps improving; check release notes per Blender/driver release for regressions/fixes.

ML tips

Batch sizing: Prefer stable throughput over maxing the card. Leave scheduler headroom for bursty traffic.
Quantization: ONNX Runtime supports popular quant formats; use INT8/FP16 variants tested on ROCm to stretch VRAM.
Containers: Freeze entire stacks in images (driver dependencies documented). Publish a “support matrix” in your job README to reduce client friction.
Multi-GPU: Use RCCL for all-reduce style workloads; mind PCIe link widths and BAR settings. Not all ML DePINs can exploit AMD multi-GPU correctly yet.

Thermals & power (for round-the-clock rigs): undervolt modestly, set sensible fan curves, and cap board power to your rack’s thermal budget. For mixed render/ML fleets, autoswitch power profiles during idle windows.

ROI & risk: choosing AMD vs. NVIDIA for DePIN income

AMD strengths

Excellent price/performance for rendering nodes (Blender, Redshift HIP).
Large VRAM options (W7900 48GB) at competitive price points for big scenes.
Open-ish stack (ROCm); good fit for bespoke inference services via ONNX Runtime.

AMD constraints

Octane on Windows/Linux remains NVIDIA-only; Render Network node path leans CUDA.
Many AI DePINs (io.net, Nosana) specify NVIDIA GPUs for workers/markets.
Windows ML stacks are workable but more fragmented (HIP subset, DirectML kernels vary).

Strategy: If your DePIN monetization is mainly rendering, AMD is a strong buy in 2025. For AI marketplace yields, NVIDIA still has the broader demand today; use AMD for direct inference contracts or Akash-style rentals while the broader ecosystem catches up.

FAQ

Can my AMD RX 7000/9000 card render Blender Cycles?

Yes. Use HIP on Windows/Linux. Ensure you’re on RDNA1+ and match Blender’s minimum driver versions. Vega is no longer supported in recent releases.

Does Redshift really work on AMD?

Yes, on Windows via HIP. Follow Maxon’s GPU list and the specific AMD Pro driver/HIP compiler versions they recommend. Validate with known scenes before rolling to your farm.

What about Octane?

Octane on Windows/Linux requires CUDA (NVIDIA). If your client or marketplace is Octane-only, plan for NVIDIA nodes. On macOS, Octane X targets Apple Silicon/Metal.

Can I earn on RNDR (Render Network) with AMD?

Practically no for standard PC nodes today, because RNDR’s node requirements are still geared to CUDA/NVIDIA. Focus AMD rigs on Cycles/Redshift clients elsewhere.

Is ML training on AMD mature enough?

For small/medium models on Linux: yes, with careful stack pinning. For big model training and marketplace monetization, NVIDIA still dominates. Inference on AMD is a smoother path (ONNX Runtime ROCm).

Windows or Linux for AMD ML?

Linux is the safer bet (ROCm full stack). Windows is better for creator dual-use rigs (Redshift/Blender) + light inference via DirectML/HIP apps. You can also use WSL for certain ROCm/ONNX paths.

AMD on DePIN: current state of ROCm and rendering vs. ML compatibility