Will It Run AI

AMD

RX 6750 XT 12GB

RX 6000ConsumerRDNA 2PCIe 4ROCm
12GB
VRAM
432GB/s
Bandwidth
30TFLOPS
FP16 Compute
240TOPS
INT8 Inference
$549 MSRP
VRAM12 GBBandwidth432 GB/sCompute30 TFInference240 TOPSValue5.46 TF/$k
RX 6750 XT 12GBCategory AvgMacBook Pro M3 Pro 18GB

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 6750 XT 12GB is the refreshed version of the 6700 XT with modestly higher clocks. It shares the same 12 GB VRAM and RDNA 2 architecture, meaning it also lacks official ROCm support. For AI inference it behaves identically to the 6700 XT from a software perspective — Vulkan via llama.cpp is the recommended path, and the clock improvement translates to marginal throughput gains.

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)Won't fitSD 3.5 Large FP16
Video Short (25f)Runs with offloadLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
no-rocmvulkan-onlygood-vram-per-dollarlegacy

规格参数

算力
FP1630 TFLOPS
INT8240 TOPS
架构RDNA 2
显存
VRAM12 GB
带宽432 GB/s
通用
系列RX 6000
定位Consumer
互连PCIe 4
计算平台ROCM
MSRP$549

核心特性

RDNA 2 architecture (Navi 22 die, refreshed)12 GB GDDR6 on a 192-bit bus432 GB/s memory bandwidth40 Compute Units at higher clocksPCIe Gen 4 x16No official ROCm — Vulkan inference recommended

AI 工作负载

优势
  • 12 GB VRAM is sufficient for 7B FP16 and 13B Q4 models
  • Slightly faster than 6700 XT thanks to clock improvements
  • Works with llama.cpp Vulkan backend out of the box
  • Used market prices are competitive
注意事项
  • No official ROCm support — identical limitations to 6700 XT
  • Vulkan inference lacks the optimization depth of CUDA
  • Clock uplift over 6700 XT is marginal for AI workloads
  • No upgrade path to better AMD AI software without new hardware

Architecture

RDNA 2

RDNA 2 is AMD's second-generation RDNA architecture, built on TSMC 7nm. It introduced hardware ray tracing and Infinity Cache for improved bandwidth efficiency. Powers the RX 6000 series and is also used in gaming consoles.

AI Relevance

Limited official ROCm support for consumer RDNA 2 cards — most AI runtimes require workarounds. Can run smaller models via llama.cpp with Vulkan or HIP backends, but performance is well behind NVIDIA equivalents.

Process: TSMC 7nmPlatform: ROCMPrecisions: FP32, FP16, INT8

购买建议

是否应该购买 RX 6750 XT 12GB 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 10 个,主要是较小的模型。较大模型需要强量化或无法适配。

12.0 GB

VRAM

$549

建议零售价

$46/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 1 additional models that do not fit on the current setup.

想要更多余量? MacBook Pro M3 Pro 18GB (18.0 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 44.8 tok/s · 32K ctx · llama.cppEST.
8.7 GB / 12.0 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 44.8 tok/s · 32K ctx · llama.cppEST.
9.8 GB / 12.0 GB VRAM

Agentic Coding

A

Gemma 4 E4B

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 38.2 tok/s · 63K ctx · llama.cppEST.
9.5 GB / 12.0 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 44.8 tok/s · 32K ctx · llama.cppEST.
9.8 GB / 12.0 GB VRAM

RAG

A

CodeGeeX 4 9B

This model is still usable for rag, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.

Decode 45.6 tok/s · 116K ctx · llama.cppEST.
8.8 GB / 12.0 GB VRAM

Full Model Compatibility

AlibabaQwen 3.5 9B
S96
9B9.8 GB45 tok/s32K ctx
dense
AlibabaQwen 3 8B
S95
8B9.2 GB50 tok/s37K ctx
dense
AlibabaQwen 3.5 4B
S92
4B6.7 GB56 tok/s54K ctx
dense
NVIDIANemotron Nano 8B
S90
8B8.9 GB50 tok/s41K ctx
dense
MicrosoftPhi-4 Mini Reasoning 4B
S88
3.8B5.9 GB53 tok/s83K ctx
dense
Jina AIJina Embeddings v3
A80
0.57B5.2 GB8 tok/s8K ctx
dense
AlibabaQwen 3 14B
A79
14B13.1 GB18 tok/s9K ctx
dense
BAAIBGE M3
A78
0.57B4.4 GB8 tok/s8K ctx
dense
MistralMinistral 3 14B
A74
14B13.1 GB18 tok/s9K ctx
multimodal
MicrosoftPhi-4-reasoning-plus 14B
A72
14.7B14.1 GB15 tok/s5K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B22.2 GB7 tok/s4K ctx
moe
AlibabaQwen 3.5 397B A17B
F0
397B247.1 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B82.5 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B619.5 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B619.5 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B866.0 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 27B
F0
27B21.7 GB3 tok/s4K ctx
dense
AlibabaQwen 3.6 27B
F0
27B19.5 GB3 tok/s4K ctx
+1dense
AlibabaQwen 3.5 122B A10B
F0
122B79.0 GB2 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B21.9 GB8 tok/s4K ctx
moe
AlibabaQwen 3.6 35B A3B
F0
35B27.6 GB4 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Flash
F0
284B161.4 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 35B A3B
F0
35B24.9 GB5 tok/s4K ctx
moe
MistralMagistral Small 2507
F0
24B19.2 GB5 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B19.2 GB5 tok/s4K ctx
dense
AlibabaQwen 3 32B
F0
32B25.5 GB2 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B22.2 GB7 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B80.1 GB2 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B73.7 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B50.9 GB2 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B78.4 GB2 tok/s4K ctx
dense
NVIDIANemotron 3 Nano 30B
F0
30B22.8 GB3 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B52.4 GB2 tok/s4K ctx
moe
MistralDevstral Small 1.1
F0
24B19.2 GB5 tok/s4K ctx
dense
Z.aiGLM-5.1
F0
754B481.1 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B83.1 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B475.0 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B411.9 GB2 tok/s4K ctx
moe
OpenAIGPT-OSS 20B
F0
21B17.4 GB15 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B148.3 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B297.8 GB2 tok/s4K ctx
moe
NVIDIANemotron Cascade 2 30B A3B
F0
30B23.3 GB7 tok/s4K ctx
moe
GoogleGemma 4 31B
F0
30.7B35.5 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B146.2 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B83.5 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B204.7 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B471.0 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B471.0 GB2 tok/s4K ctx
moe
LG AIEXAONE 4.0 32B
F0
32B25.5 GB2 tok/s4K ctx
dense
GoogleGemma 4 26B A4B
F0
25.2B21.1 GB9 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

24 of 52 models can generate images or video on your RX 6750 XT 12GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~2sS
Stable Diffusion 1.5Image512×768~3.9sS
Realistic Vision v5.1Image512×768~3.9sS
DreamShaper 8Image512×768~3.9sS
LCM DreamShaper v7Image512×768~1.2sS
PixArt-SigmaImage256×256~1m 11sS
FramePack I2VVideo256×256~28.8s/frameS
SDXL TurboImage512×512~2sS
SDXL LightningImage1024×1024~5.9sS
Stable Diffusion XL 1.0Image1024×1024~15.7sS
Playground v2.5Image1024×1024~23.5sS
RealVisXL v5.0Image1024×1024~17.6sS
DreamShaper XLImage1024×1024~17.6sS
Juggernaut XL v9Image1024×1024~17.6sS
Animagine XL 3.1Image1024×1024~17.6sS
Pony Diffusion V6 XLImage1024×1024~17.6sS
Animagine XL 4.0Image1024×1024~17.6sS
Illustrious XLImage1024×1024~17.6sS
Wan Video 2.1 1.3BVideo256×256~11.5s/frameA
Stable Diffusion 3.5 MediumImage256×256~27.4sA
Flux.2 Klein 4BImage256×256~10.6sA
LTX Video 2BVideo256×256~13.6s/frameB
KolorsImage256×256~31.4sB
Stable CascadeImage1024×1024~39.2sD
AuraFlow v0.3Image256×256~1m 11sF
Stable Diffusion 3.5 LargeImage256×256~1m 26sF
Stable Diffusion 3.5 Large TurboImage256×256~15.7sF
CogVideoX 2BVideo256×256~13.6s/frameF
HunyuanVideoVideo256×256~28.8s/frameF
ChromaImage256×256~15.7sF
Z-Image TurboImage256×256~16.2sF
Flux.1 DevImage256×256~1m 11sF
Flux.1 SchnellImage256×256~13.7sF
LTX Video 13BVideo256×256~28.8s/frameF
Flux.1 Kontext DevImage256×256~1m 18sF
AnimateDiff v1.5.3Video512×768~7.2s/frameF
Cosmos Diffusion 7BVideo256×256~22.5s/frameF
CogVideoX 5BVideo256×256~19.6s/frameF
Wan2.2 TI2V 5BVideo256×256~19.6s/frameF
Flux.2 Klein 9BImage256×256~7.8sF
Flux.1 Fill DevImage256×256~1m 7sF
Mochi 1 PreviewVideo256×256~25.9s/frameF
HunyuanVideo 1.5Video256×256~24.1s/frameF
Helios 14BVideo256×256~29.6s/frameF
SkyReels V2 14BVideo256×256~29.6s/frameF
Wan Video 2.1 14BVideo256×256~29.6s/frameF
Wan Video 2.2 14BVideo256×256~29.6s/frameF
Qwen ImageImage256×256~26.4sF
Qwen Image EditImage256×256~26.4sF
Flux.2 DevImage256×256~12m 22sF
MAGI-1Video256×256~36.8s/frameF
HunyuanImage 3.0Image256×256~46.5sF

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 6750 XT 12GB

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 6750 XT 12GB?

RX 6750 XT 12GB (12 GB VRAM) can run these top models: Qwen 3.5 9B (score: 96/100), Qwen 3 8B (score: 95/100), Qwen 3.5 4B (score: 92/100). See the full compatibility list above.

How much VRAM does RX 6750 XT 12GB have for AI?

RX 6750 XT 12GB has 12 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is RX 6750 XT 12GB good for running LLMs locally?

Yes, RX 6750 XT 12GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RX 6750 XT 12GB for coding?

For coding on RX 6750 XT 12GB, we recommend Qwen 3.5 9B. It achieves 44.8 tokens per second with 32K 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 6750 XT 12GB?

There are 4 upgrade path(s) from RX 6750 XT 12GB: MacBook Pro M3 Pro 18GB, RX 6800 XT 16GB. Upgrading would unlock larger models and faster inference speeds.

Can RX 6750 XT 12GB run Flux for image generation?

RX 6750 XT 12GB 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 6750 XT 12GB?

RX 6750 XT 12GB (12 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 6750 XT 12GB good for AI image generation?

RX 6750 XT 12GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 12 GB of usable memory is sufficient for most image generation workflows at standard resolutions.

Can RX 6750 XT 12GB run Qwen 3.5 27B?

Qwen 3.5 27B does not fit on RX 6750 XT 12GB with 12 GB. However, Qwen 3.5 9B at Q4 (5.5 GB) or Q5 (6.5 GB) runs well on your GPU. The 4B variant fits at Q8 for near-lossless quality.

What is the best quantization for AI models on RX 6750 XT 12GB?

With 12 GB on RX 6750 XT 12GB, use Q4_K_M for 8B models and Q4_K_M with tight context for 14B models. Q5_K_M is a good middle ground when the model fits. For the best quality-to-size ratio, Q4_K_M is the most popular choice.

For local LLMs on RX 6750 XT 12GB, does VRAM matter more than bandwidth?

On RX 6750 XT 12GB, capacity is usually the first gate: if the model does not fit, bandwidth does not matter. But once a model fits, memory bandwidth is what largely determines tokens per second. In practice, you want enough memory to fit the model plus headroom, then as much bandwidth as your budget allows.

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