AMD
Operating mode
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.
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
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
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.
购买建议
有限制地可用于本地 AI
可运行 50 个顶级模型中的 11 个，主要是较小的模型。较大模型需要强量化或无法适配。
16.0 GB
VRAM
$479
建议零售价
$30/GB
每 GB VRAM 成本
最适合此 GPU 的模型
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.
想要更多余量？ MacBook Pro M3 24GB (24.0 GB unified memory) 是下一步升级选择。
Chat
SQwen 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.
Coding
SQwen 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.
Agentic Coding
SQwen 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.
Reasoning
SQwen 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.
RAG
AGranite 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.
Qwen 3.5 9BQwen 3 8BQwen 3 14B
Phi-4-reasoning-plus 14BQwen 3.5 4B
Nemotron Nano 8B
Ministral 3 14BPhi-4 Mini Reasoning 4B
GPT-OSS 20B
Jina Embeddings v3
BGE M3Qwen3-Coder 30B A3B InstructQwen 3.5 397B A17BDevstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 27BQwen 3.6 27BQwen 3.5 122B A10BQwen3-VL 30B A3B InstructQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 35B A3BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 32BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72BGPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671B
EXAONE 4.0 32BGemma 4 26B A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
31 of 52 models can generate images or video on your RX 9070 16GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1s | S |
| Stable Diffusion 1.5Image | 512×768 | ~2.1s | S |
| Realistic Vision v5.1Image | 512×768 | ~2.1s | S |
| DreamShaper 8Image | 512×768 | ~2.1s | S |
| LCM DreamShaper v7Image | 512×768 | 600ms | S |
| PixArt-SigmaImage | 1024×1024 | ~8.4s | S |
| FramePack I2VVideo | 256×256 | ~15.4s/frame | S |
| SDXL TurboImage | 512×512 | ~1s | S |
| SDXL LightningImage | 1024×1024 | ~3.1s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~8.4s | S |
| Playground v2.5Image | 1024×1024 | ~12.6s | S |
| RealVisXL v5.0Image | 1024×1024 | ~9.4s | S |
| DreamShaper XLImage | 1024×1024 | ~9.4s | S |
| Juggernaut XL v9Image | 1024×1024 | ~9.4s | S |
| Animagine XL 3.1Image | 1024×1024 | ~9.4s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~9.4s | S |
| Animagine XL 4.0Image | 1024×1024 | ~9.4s | S |
| Illustrious XLImage | 1024×1024 | ~9.4s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~6.1s/frame | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~44s | S |
| Flux.2 Klein 4BImage | 256×256 | ~5.7s | S |
| LTX Video 2BVideo | 256×256 | ~7.3s/frame | S |
| KolorsImage | 256×256 | ~44.5s | A |
| Stable CascadeImage | 1024×1024 | ~20.9s | B |
| AuraFlow v0.3Image | 256×256 | ~1m 14s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~2m 4s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~22.6s | B |
| CogVideoX 2BVideo | 256×256 | ~7.3s/frame | D |
| HunyuanVideoVideo | 256×256 | ~15.4s/frame | D |
| ChromaImage | 256×256 | ~8.4s | D |
| Z-Image TurboImage | 256×256 | ~17.3s | D |
| Flux.1 DevImage | 256×256 | ~37.7s | F |
| Flux.1 SchnellImage | 256×256 | ~7.3s | F |
| LTX Video 13BVideo | 256×256 | ~15.4s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~41.9s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~3.8s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~12s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~10.5s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~10.5s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~4.2s | F |
| Flux.1 Fill DevImage | 256×256 | ~35.6s | F |
| Mochi 1 PreviewVideo | 256×256 | ~13.8s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~12.9s/frame | F |
| Helios 14BVideo | 256×256 | ~15.8s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~15.8s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~15.8s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~15.8s/frame | F |
| Qwen ImageImage | 256×256 | ~14.1s | F |
| Qwen Image EditImage | 256×256 | ~14.1s | F |
| Flux.2 DevImage | 256×256 | ~6m 36s | F |
| MAGI-1Video | 256×256 | ~19.6s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~24.8s | 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.
Upgrade paths
See what you unlock with more powerful hardware
升级选项
Unlocks 2 additional models that do not fit on the current setup.
~$1,099 MSRP
Intel Arc Pro B60 24GB最佳性价比Unlocks 36 additional models that do not fit on the current setup.
~$599 MSRP
Unlocks 36 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 23%.
~$999 MSRP
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 165%.
~$8,000 MSRP
RX 9070 16GB (16 GB VRAM) can run these top models: Qwen 3.5 9B (score: 97/100), Qwen 3 8B (score: 95/100), Qwen 3 14B (score: 93/100). See the full compatibility list above.
RX 9070 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RX 9070 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RX 9070 16GB, we recommend Qwen 3.5 9B. It achieves 77.7 tokens per second with 58K context window. 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.
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.
RX 9070 16GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.
RX 9070 16GB (16 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
RX 9070 16GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 16 GB of usable memory is sufficient for most image generation workflows at standard resolutions.
Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for RX 9070 16GB with 16 GB. You can run the 9B variant at Q8 (9.6 GB) for excellent quality, or try the 35B-A3B MoE variant at Q4 if it fits your context needs.
With 16 GB on RX 9070 16GB, use Q8_0 for 8B models (best quality), Q4_K_M for 14B models (good balance), and Q4_K_M with limited context for larger models. Avoid going below Q4 — quality drops sharply at Q2-Q3.
RX 9070 16GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.
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