RX 9000ConsumerRDNA 4PCIe 5ROCm
Operating mode
Choose the operating mode for this hardware
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 9070 16GB is AMD's mainstream RDNA 4 offering, delivering meaningfully improved AI compute efficiency compared to RDNA 3 at a competitive $479 price. The 16 GB of GDDR6 VRAM and PCIe Gen 5 connectivity make it future-ready. ROCm support for RDNA 4 is anticipated with AMD's continued push into AI, but as of early 2026 the ecosystem is still in early stages — Linux-focused users willing to be early adopters will find the hardware capable.
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-earlygood-valuelatest-gen
Specifications
Compute
FP1648 TFLOPS
INT8384 TOPS
ArchitectureRDNA 4
Memory
VRAM16 GB
Bandwidth640 GB/s
TypeGDDR6
General
FamilyRX 9000
SegmentConsumer
InterconnectPCIe 5
Compute PlatformROCM
MSRP$479
TDP200W
Key Features
RDNA 4 architecture (Navi 48 die)16 GB GDDR6 on a 256-bit bus640 GB/s memory bandwidthPCIe Gen 5 x16Improved matrix/AI acceleration units vs RDNA 3ROCm support expected — verify current status
For AI Workloads
Strengths
- 640 GB/s bandwidth on 16 GB is solid — competitive decode throughput
- PCIe Gen 5 enables faster CPU-GPU data transfers for pipeline workloads
- RDNA 4 delivers better performance-per-watt than RDNA 3 for AI workloads
- 16 GB VRAM enables 13B Q4 and limited 34B Q4 models
Considerations
- RDNA 4 ROCm ecosystem is early — not fully stabilized as of early 2026
- Framework support (PyTorch, ONNX Runtime) requires validation on RDNA 4
- NVIDIA RTX 5070 offers similar compute with more mature CUDA support
- Early adopters may encounter missing kernel implementations in ROCm
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 9B 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 60.7 tok/s · 45K ctx · llama.cppEST.
Qwen 3.5 9B is a specialized fit for 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 77.7 tok/s · 58K 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 ~21.8 GB
397BTier 100Needs ~246.1 GB
123BTier 100Needs ~80.2 GB
1000BTier 100Needs ~616.2 GB
1000BTier 100Needs ~616.2 GB
Image & Video Generation
Diffusion Model Compatibility
31 of 52 models can generate images or video on your RX 9070 16GB
Upgrade paths
Upgrade from RX 9070 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
RX 9070 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~8.4s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~37.7s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~2m 4s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~7.3s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~21.4s/frame |
Qwen 3.5 9B 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 77.7 tok/s · 58K ctx · llama.cppEST.
Qwen 3.5 9B matches Reasoning 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 60.7 tok/s · 45K ctx · llama.cppEST.
Granite 4.1 8B 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 87.4 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
50 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~19.6s/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 9070 16GB: MacBook Pro M3 24GB, Intel Arc Pro B60 24GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy RX 9070 16GB for local AI?
Usable for local AI with limits
Can run 11 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 2 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.
