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 XT 16GB is AMD's high-performance RDNA 4 mainstream GPU, competing with NVIDIA's RTX 5070 class at $549. Its 16 GB of GDDR6 VRAM and 640 GB/s bandwidth are comparable to the RX 9070, but with higher compute throughput (58 vs 48 TFLOPS FP16). ROCm support for RDNA 4 is expected and AMD is actively investing in consumer ROCm, though the ecosystem is still maturing in early 2026.
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-earlyhigh-performancelatest-gen
Specifications
Compute
FP1658 TFLOPS
INT8464 TOPS
ArchitectureRDNA 4
Memory
VRAM16 GB
Bandwidth640 GB/s
TypeGDDR6
General
FamilyRX 9000
SegmentConsumer
InterconnectPCIe 5
Compute PlatformROCM
MSRP$549
TDP250W
Key Features
RDNA 4 architecture (Navi 48 die, higher configuration)16 GB GDDR6 on a 256-bit bus640 GB/s memory bandwidthPCIe Gen 5 x1658 TFLOPS FP16 computeROCm support expected on RDNA 4
For AI Workloads
Strengths
- High compute (58 TFLOPS) enables fast prefill and generation throughput
- 16 GB VRAM and 640 GB/s bandwidth is competitive with RTX 5070
- PCIe Gen 5 reduces bottlenecks for large model loading
- RDNA 4 efficiency improvements over RDNA 3 benefit inference workloads
Considerations
- RDNA 4 ROCm is in early stages — full framework parity with CUDA not yet achieved
- ROCm remains Linux-only, limiting Windows AI users to Vulkan inference
- Some specialized AI kernels (FlashAttention, etc.) may lack RDNA 4 optimization
- Investment in RDNA 4 AI tooling is still ramping up
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 62.6 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 80.2 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 XT 16GB
Upgrade paths
Upgrade from RX 9070 XT 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
RX 9070 XT 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~6.7s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~30.2s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~1m 40s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~5.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~17.2s/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 80.2 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 62.6 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 90.2 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
52 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~15.8s/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.
Buying advice
Should you buy RX 9070 XT 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.
