Radeon R9 390
Everyday Tasks
Evaluation - Excellent
Compared with GeForce GT 1030, a baseline GPU for everyday tasks such as Office work and web browsing, it delivers +266% performance.
Light Gaming
Evaluation - Usable
Compared with GeForce GTX 1650, a baseline GPU for light games such as Valorant, Minecraft, Roblox, UNDERTALE, and League of Legends at FHD / 120 FPS, it delivers +12% performance.
Mid-range Gaming
Evaluation - Difficult
Compared with GeForce RTX 3060 8GB, a baseline GPU for mid-range games such as Apex Legends, Overwatch, Fortnite, and Dead by Daylight at FHD / 120 FPS, it delivers -42% performance.
Heavy Gaming
Evaluation - Difficult
Compared with GeForce RTX 3070, a baseline GPU for heavy games such as Battlefield 6, Cyberpunk 2077, Escape from Tarkov, and Monster Hunter Wilds at FHD / 120 FPS, it delivers -60% performance.
1080p Video Editing
Evaluation - Usable
Compared with GeForce GTX 1650, a baseline GPU for 1080p video editing in tools like Premiere Pro, DaVinci Resolve, and Filmora, it delivers +12% performance.
4K Video Editing
Evaluation - Difficult
Compared with GeForce RTX 3060 12GB, a baseline GPU for 4K video editing in Premiere Pro, DaVinci Resolve Studio, and After Effects, it delivers -47% performance.
FHD Image Editing
Evaluation - Strong
Compared with GeForce GTX 1050 Ti, a baseline GPU for FHD image editing in Photoshop, Lightroom, Figma, and Clip Studio Paint, it delivers +38% performance.
3D CG Creation
Evaluation - Difficult
Compared with GeForce RTX 2060, a baseline GPU for 3D CG work (modeling and viewport preview) in tools like Blender, Maya, and 3ds Max, it delivers -38% performance.
Game Development
Evaluation - Difficult
Compared with GeForce RTX 3060 12GB, a baseline GPU for game development (editor work and in-editor preview) in Unity, Unreal Engine 5, and Godot Engine, it delivers -47% performance.
CAD
Evaluation - Usable
Compared with GeForce GTX 1650, a baseline GPU for CAD work in tools like AutoCAD and Fusion 360, it delivers +12% performance.
Local LLM
Evaluation - Not applicable
Launched in 2015, its design predates the generations current AI tooling assumes (2018 or later), so it is outside the scope of this quick check.
As a rule of thumb, (quantized) 8B models such as Gemma, Qwen, Llama, and DeepSeek need 8 GB of VRAM, 22B models need 16 GB, and 32B models need 24 GB. At the same VRAM capacity, newer generations also run faster. The field moves fast, so this is a quick check of where Radeon R9 390's VRAM stands.
AI Image Generation
Evaluation - Not applicable
Launched in 2015, its design predates the generations current AI tooling assumes (2018 or later), so it is outside the scope of this quick check.
As a rule of thumb, (quantized) 2–6B models such as Krea, Flux, SDXL, Pony, Anima, and Illustrious need 8 GB of VRAM, 12B models need 16 GB, and 20B models need 24 GB. At the same VRAM capacity, newer generations also run faster. The field moves fast, so this is a quick check of where Radeon R9 390's VRAM stands.
Note: many tools in this field assume CUDA, so non-NVIDIA GPUs face extra hurdles to get running.
AI Video Generation
Evaluation - Not applicable
Launched in 2015, its design predates the generations current AI tooling assumes (2018 or later), so it is outside the scope of this quick check.
As a rule of thumb, (quantized) models such as Wan, LTX, and Hunyuan need at least 16 GB of VRAM for practical use. At the same VRAM capacity, newer generations also run faster. The field moves fast, so this is a quick check of where Radeon R9 390's VRAM stands.
Note: many tools in this field assume CUDA, so non-NVIDIA GPUs face extra hurdles to get running.
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Radeon R9 390
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