RX 9000ConsumerRDNA 4PCIe 4ROCm
Operating mode
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Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The RX 9060 8GB is an entry-level RDNA 4 card, AMD's newest consumer architecture as of 2025. RDNA 4 is expected to have ROCm support but the ecosystem is still early — driver stability and framework compatibility should be verified before assuming production-ready AI use. Its 8 GB of GDDR6 VRAM limits it to smaller models, but the RDNA 4 architecture brings improved compute efficiency over RDNA 3.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
rdna4-earlybudget-friendlysoftware-limited
Specifications
Compute
FP1645 TFLOPS
INT890 TOPS
ArchitectureRDNA 4
Memory
VRAM8 GB
Bandwidth288 GB/s
General
FamilyRX 9000
SegmentConsumer
InterconnectPCIe 4
Compute PlatformROCM
MSRP$249
Key Features
RDNA 4 architecture (Navi 44 die)8 GB GDDR6 on a 128-bit bus288 GB/s memory bandwidthPCIe Gen 4 x8Improved AI accelerators vs RDNA 3ROCm support expected — ecosystem still maturing
For AI Workloads
Strengths
- RDNA 4 brings improved AI accelerator efficiency over prior generations
- Budget-friendly entry point ($249) for AMD's latest architecture
- ROCm support expected on RDNA 4 — better long-term software prospects than RDNA 2
- Low power draw for a modern GPU
Considerations
- RDNA 4 ROCm ecosystem is very new — verify compatibility before purchasing for AI
- 8 GB VRAM caps usable models at 7B Q4 — a hard limitation
- Framework coverage for RDNA 4 ROCm lags behind NVIDIA CUDA
- Early adopters may face driver and kernel compatibility issues
RDNA 4 is AMD's latest GPU architecture built on TSMC 4nm. It focuses on efficiency and ray tracing improvements with enhanced AI processing capabilities.
AI Relevance
Improved ROCm support and new AI accelerators with FP8 support bring AMD closer to competitive AI inference performance. The focus on efficiency makes RDNA 4 GPUs attractive for power-constrained deployments.
Process: TSMC 4nmPlatform: ROCMPrecisions: FP32, FP16, BF16, FP8, INT8
Recommendations by Workload
Qwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 56.0 tok/s · 22K ctx · llama.cppEST.
Codestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 48.9 tok/s · 67K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.0 GB
397BTier 100Needs ~245.3 GB
123BTier 100Needs ~79.4 GB
1000BTier 100Needs ~615.4 GB
1000BTier 100Needs ~615.4 GB
Image & Video Generation
Diffusion Model Compatibility
21 of 52 models can generate images or video on your RX 9060 8GB
Upgrade paths
Upgrade from RX 9060 8GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
RX 9060 8GBCategory AvgRTX 3080 10GB
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~23.3s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~39.6s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~48.4s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~7.6s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~22.5s/frame |
Gemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 48.1 tok/s · 96K ctx · llama.cppEST.
Codestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 48.9 tok/s · 67K ctx · llama.cppEST.
Granite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 42.0 tok/s · 59K ctx · llama.cppEST.
Image
| MAGI-1Video | 256×256 | ~20.6s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
There are 4 upgrade path(s) from RX 9060 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy RX 9060 8GB for local AI?
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.