Will It Run AI

AMD

RX 9060 XT 16GB

RX 9000ConsumerRDNA 4PCIe 4ROCm
16GB
VRAM
320GB/s
Bandwidth
51TFLOPS
FP16 Compute
102TOPS
INT8 Inference
$349 MSRP
VRAM16 GBBandwidth320 GB/sCompute51 TFInference102 TOPSValue14.61 TF/$k
RX 9060 XT 16GBCategory AvgMacBook Pro M3 24GB

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 9060 XT 16GB is a mid-range RDNA 4 card offering 16 GB of GDDR6 VRAM at a competitive $349 price point. RDNA 4 represents AMD's latest architecture with improved compute efficiency. ROCm support is expected and AMD has been active in extending ROCm coverage to new consumer GPUs, but as of early 2026 the ecosystem is still maturing. The 16 GB VRAM enables 13B models at Q4 and makes this a promising option once software stabilizes.

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle — from text generation to image and video creation.

CapabilityStatusRepresentative Model
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Won’t fitQwen 3 30B Q4
LLM Large (70B)Won’t fitLlama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16
Image Gen (Flux)Won't fitFlux.1 Dev FP16
Image Gen (SD 3.5)Runs with sequential offloadSD 3.5 Large FP16
Video Short (25f)Runs nativelyLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
rdna4-earlygood-vram-per-dollarsoftware-limited

规格参数

算力
FP1651 TFLOPS
INT8102 TOPS
架构RDNA 4
显存
VRAM16 GB
带宽320 GB/s
通用
系列RX 9000
定位Consumer
互连PCIe 4
计算平台ROCM
MSRP$349

核心特性

RDNA 4 architecture (Navi 44 die)16 GB GDDR6 on a 128-bit bus320 GB/s memory bandwidthPCIe Gen 4 x8Enhanced AI accelerators vs RDNA 3ROCm support expected — verify before use

AI 工作负载

优势
  • 16 GB VRAM at $349 is exceptional value — matches higher-end cards in capacity
  • RDNA 4 architecture improves AI instruction throughput over RDNA 3
  • ROCm support expected with AMD's continued consumer ecosystem investment
  • Vulkan backend available as fallback while ROCm matures
注意事项
  • RDNA 4 ROCm support is early — production AI workloads may hit edge cases
  • Narrow 128-bit bus means 320 GB/s bandwidth is modest for 16 GB capacity
  • Framework ecosystem (PyTorch, etc.) not fully validated on RDNA 4
  • Similar NVIDIA options have more mature CUDA support

Architecture

RDNA 4

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

购买建议

是否应该购买 RX 9060 XT 16GB 用于本地 AI?

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

$349

建议零售价

$22/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。

Recommendations by Workload

Chat

S

Qwen 3.5 9B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.5 tok/s · 58K ctx · llama.cppEST.
9.1 GB / 16.0 GB VRAM

Coding

S

Qwen 3.5 9B

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.5 tok/s · 58K ctx · llama.cppEST.
10.2 GB / 16.0 GB VRAM

Agentic Coding

S

Qwen 3.5 9B

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.5 tok/s · 58K ctx · llama.cppEST.
12.4 GB / 16.0 GB VRAM

Reasoning

S

Qwen 3.5 9B

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.5 tok/s · 58K ctx · llama.cppEST.
10.2 GB / 16.0 GB VRAM

RAG

A

Granite 4.1 8B

This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.

Decode 44.4 tok/s · 56K ctx · llama.cppEST.
12.3 GB / 16.0 GB VRAM

Full Model Compatibility

AlibabaQwen 3.5 9B
S94
9B10.2 GB40 tok/s58K ctx
dense
AlibabaQwen 3 8B
S92
8B9.6 GB44 tok/s63K ctx
dense
AlibabaQwen 3 14B
S90
14B13.5 GB26 tok/s33K ctx
dense
AlibabaQwen 3.5 4B
S90
4B7.1 GB56 tok/s81K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
S89
14.7B14.5 GB24 tok/s24K ctx
dense
NVIDIANemotron Nano 8B
S87
8B9.3 GB44 tok/s71K ctx
dense
MicrosoftPhi-4 Mini Reasoning 4B
S86
3.8B6.3 GB53 tok/s122K ctx
dense
MistralMinistral 3 14B
A85
14B13.5 GB25 tok/s33K ctx
multimodal
Jina AIJina Embeddings v3
A78
0.57B5.6 GB8 tok/s8K ctx
dense
OpenAIGPT-OSS 20B
A78
21B17.8 GB23 tok/s5K ctx
moe
BAAIBGE M3
A77
0.57B4.8 GB8 tok/s8K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B22.6 GB11 tok/s4K ctx
moe
AlibabaQwen 3.5 397B A17B
F0
397B247.5 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B82.9 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B619.9 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B619.9 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B866.4 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 27B
F0
27B22.1 GB5 tok/s4K ctx
dense
AlibabaQwen 3.6 27B
F0
27B19.9 GB5 tok/s4K ctx
+1dense
AlibabaQwen 3.5 122B A10B
F0
122B79.4 GB2 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B22.3 GB12 tok/s4K ctx
moe
AlibabaQwen 3.6 35B A3B
F0
35B28.0 GB6 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Flash
F0
284B161.8 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 35B A3B
F0
35B25.3 GB8 tok/s4K ctx
moe
MistralMagistral Small 2507
F0
24B19.6 GB7 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B19.6 GB7 tok/s4K ctx
dense
AlibabaQwen 3 32B
F0
32B25.9 GB3 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B22.6 GB11 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B80.5 GB2 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B74.1 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B51.3 GB2 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B78.8 GB2 tok/s4K ctx
dense
NVIDIANemotron 3 Nano 30B
F0
30B23.2 GB4 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B52.8 GB2 tok/s4K ctx
moe
MistralDevstral Small 1.1
F0
24B19.6 GB7 tok/s4K ctx
dense
Z.aiGLM-5.1
F0
754B481.5 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B83.5 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B475.4 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B412.3 GB2 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B148.7 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B298.2 GB2 tok/s4K ctx
moe
NVIDIANemotron Cascade 2 30B A3B
F0
30B23.7 GB10 tok/s4K ctx
moe
GoogleGemma 4 31B
F0
30.7B35.9 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B146.6 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B83.9 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B205.1 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
LG AIEXAONE 4.0 32B
F0
32B25.9 GB3 tok/s4K ctx
dense
GoogleGemma 4 26B A4B
F0
25.2B21.5 GB13 tok/s4K ctx
moe

触手可及

升级后即可运行的模型

高质量模型,只需稍多一点内存

Image & Video Generation

Diffusion Model Compatibility

31 of 52 models can generate images or video on your RX 9060 XT 16GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~1sS
Stable Diffusion 1.5Image512×768~1.9sS
Realistic Vision v5.1Image512×768~1.9sS
DreamShaper 8Image512×768~1.9sS
LCM DreamShaper v7Image512×768600msS
PixArt-SigmaImage1024×1024~7.8sS
FramePack I2VVideo256×256~14.2s/frameS
SDXL TurboImage512×512~1sS
SDXL LightningImage1024×1024~2.9sS
Stable Diffusion XL 1.0Image1024×1024~7.8sS
Playground v2.5Image1024×1024~11.6sS
RealVisXL v5.0Image1024×1024~8.7sS
DreamShaper XLImage1024×1024~8.7sS
Juggernaut XL v9Image1024×1024~8.7sS
Animagine XL 3.1Image1024×1024~8.7sS
Pony Diffusion V6 XLImage1024×1024~8.7sS
Animagine XL 4.0Image1024×1024~8.7sS
Illustrious XLImage1024×1024~8.7sS
Wan Video 2.1 1.3BVideo256×256~5.7s/frameS
Stable Diffusion 3.5 MediumImage256×256~40.7sS
Flux.2 Klein 4BImage256×256~5.2sS
LTX Video 2BVideo256×256~6.7s/frameS
KolorsImage256×256~41.2sA
Stable CascadeImage1024×1024~19.4sB
AuraFlow v0.3Image256×256~1m 9sB
Stable Diffusion 3.5 LargeImage256×256~1m 55sB
Stable Diffusion 3.5 Large TurboImage256×256~20.9sB
CogVideoX 2BVideo256×256~6.7s/frameD
HunyuanVideoVideo256×256~14.2s/frameD
ChromaImage256×256~7.8sD
Z-Image TurboImage256×256~16sD
Flux.1 DevImage256×256~34.9sF
Flux.1 SchnellImage256×256~6.8sF
LTX Video 13BVideo256×256~14.2s/frameF
Flux.1 Kontext DevImage256×256~38.8sF
AnimateDiff v1.5.3Video512×768~3.5s/frameF
Cosmos Diffusion 7BVideo256×256~11.1s/frameF
CogVideoX 5BVideo256×256~9.7s/frameF
Wan2.2 TI2V 5BVideo256×256~9.7s/frameF
Flux.2 Klein 9BImage256×256~3.9sF
Flux.1 Fill DevImage256×256~33sF
Mochi 1 PreviewVideo256×256~12.8s/frameF
HunyuanVideo 1.5Video256×256~11.9s/frameF
Helios 14BVideo256×256~14.7s/frameF
SkyReels V2 14BVideo256×256~14.7s/frameF
Wan Video 2.1 14BVideo256×256~14.7s/frameF
Wan Video 2.2 14BVideo256×256~14.7s/frameF
Qwen ImageImage256×256~13.1sF
Qwen Image EditImage256×256~13.1sF
Flux.2 DevImage256×256~6m 7sF
MAGI-1Video256×256~18.2s/frameF
HunyuanImage 3.0Image256×256~23sF

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

Upgrade from RX 9060 XT 16GB

See what you unlock with more powerful hardware

升级选项

升级选项

Frequently Asked Questions

What AI models can I run on RX 9060 XT 16GB?

RX 9060 XT 16GB (16 GB VRAM) can run these top models: Qwen 3.5 9B (score: 94/100), Qwen 3 8B (score: 92/100), Qwen 3 14B (score: 90/100). See the full compatibility list above.

How much VRAM does RX 9060 XT 16GB have for AI?

RX 9060 XT 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is RX 9060 XT 16GB good for running LLMs locally?

Yes, RX 9060 XT 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RX 9060 XT 16GB for coding?

For coding on RX 9060 XT 16GB, we recommend Qwen 3.5 9B. It achieves 39.5 tokens per second with 58K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Should I upgrade from RX 9060 XT 16GB?

There are 4 upgrade path(s) from RX 9060 XT 16GB: MacBook Pro M3 24GB, Intel Arc Pro B60 24GB. Upgrading would unlock larger models and faster inference speeds.

Can RX 9060 XT 16GB run Flux for image generation?

RX 9060 XT 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.

What image and video AI models can I run on RX 9060 XT 16GB?

RX 9060 XT 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.

Is RX 9060 XT 16GB good for AI image generation?

RX 9060 XT 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.

Can RX 9060 XT 16GB run Qwen 3.5 27B?

Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for RX 9060 XT 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.

What is the best quantization for AI models on RX 9060 XT 16GB?

With 16 GB on RX 9060 XT 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.

For local LLMs on RX 9060 XT 16GB, does VRAM matter more than bandwidth?

RX 9060 XT 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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