AMD

RX 6650 XT 8GB

RX 6000ConsumerRDNA 2PCIe 4ROCm
8GB
VRAM
280GB/s
Bandwidth
22TFLOPS
FP16 Compute
176TOPS
INT8 Inference
$399 MSRP
VRAM8 GBBandwidth280 GB/sCompute22 TFInference176 TOPSValue5.51 TF/$k
RX 6650 XT 8GBCategory AvgRTX 3080 10GB

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 6650 XT 8GB is a refreshed RDNA 2 card with slightly higher clocks than the 6600 XT. Like all RDNA 2 consumer cards, it has no official ROCm support — AI inference runs through Vulkan backends in llama.cpp. It can fit 7B models at Q4, making it workable for basic local inference, but the software ecosystem is significantly weaker than comparable NVIDIA options.

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 with sequential offloadSDXL 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)Won't fitLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
no-rocmvulkan-onlybudget-friendlylegacy

Spezifikationen

Rechenleistung
FP1622 TFLOPS
INT8176 TOPS
ArchitekturRDNA 2
Speicher
VRAM8 GB
Bandbreite280 GB/s
Allgemein
FamilieRX 6000
SegmentConsumer
InterconnectPCIe 4
Compute-PlattformROCM
MSRP$399

Hauptmerkmale

RDNA 2 architecture (Navi 23 die, refreshed)8 GB GDDR6 on a 128-bit bus280 GB/s memory bandwidth32 Compute Units at higher clocks vs 6600 XTPCIe Gen 4 x8 (electrical)No official ROCm support — Vulkan inference only

Für KI-Workloads

Stärken
  • Slightly faster than 6600 XT at the same VRAM capacity
  • 8 GB is enough for 7B models at Q4 quantization
  • Works with llama.cpp Vulkan backend without ROCm
  • Low TDP suitable for compact desktop builds
Hinweise
  • No official ROCm support — RDNA 2 consumer cards are excluded
  • 8 GB ceiling means 13B+ models require CPU offloading or are out of reach
  • Vulkan inference is slower and less reliable than CUDA or ROCm
  • Minor clock bump over 6600 XT is rarely meaningful for AI workloads

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

Kaufberatung

Sollten Sie RX 6650 XT 8GB für lokale KI kaufen?

Nutzbar für lokale KI mit Einschränkungen

Kann 7 von 50 Top-Modellen ausführen, hauptsächlich kleinere. Größere Modelle benötigen starke Quantisierung oder passen nicht.

8.0 GB

VRAM

$399

UVP

$50/GB

Kosten pro GB VRAM

Beste Modelle für diese GPU

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 33 additional models that do not fit on the current setup.

Mehr Spielraum gewünscht? RTX 3080 10GB (10.0 GB VRAM) ist die nächste Stufe.

Recommendations by Workload

Chat

S

Qwen 3.5 4B

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 56.0 tok/s · 28K ctx · llama.cppEST.
5.2 GB / 8.0 GB VRAM

Coding

S

Qwen 3.5 4B

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 56.0 tok/s · 28K ctx · llama.cppEST.
6.3 GB / 8.0 GB VRAM

Agentic Coding

A

Gemma 4 E2B

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 37.8 tok/s · 96K ctx · llama.cppEST.
5.9 GB / 8.0 GB VRAM

Reasoning

S

Phi-4 Mini Reasoning 4B

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.

Decode 53.2 tok/s · 43K ctx · llama.cppEST.
5.5 GB / 8.0 GB VRAM

RAG

A

Granite 4.1 3B

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 42.0 tok/s · 59K ctx · llama.cppEST.
6.0 GB / 8.0 GB VRAM

Full Model Compatibility

AlibabaQwen 3.5 4B
S95
4B6.3 GB56 tok/s28K ctx
dense
MicrosoftPhi-4 Mini Reasoning 4B
S92
3.8B5.5 GB53 tok/s43K ctx
dense
Jina AIJina Embeddings v3
A84
0.57B4.8 GB8 tok/s8K ctx
dense
BAAIBGE M3
A81
0.57B4.0 GB8 tok/s8K ctx
dense
AlibabaQwen 3.5 9B
A80
9B9.4 GB15 tok/s6K ctx
dense
AlibabaQwen 3 8B
A79
8B8.8 GB19 tok/s10K ctx
dense
NVIDIANemotron Nano 8B
A74
8B8.5 GB21 tok/s12K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.8 GB3 tok/s4K ctx
moe
AlibabaQwen 3.5 397B A17B
F0
397B246.7 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B82.1 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B619.1 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B619.1 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B865.6 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 27B
F0
27B21.3 GB2 tok/s4K ctx
dense
AlibabaQwen 3.6 27B
F0
27B19.1 GB2 tok/s4K ctx
+1dense
AlibabaQwen 3.5 122B A10B
F0
122B78.6 GB2 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B21.5 GB3 tok/s4K ctx
moe
AlibabaQwen 3.6 35B A3B
F0
35B27.2 GB3 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Flash
F0
284B161.0 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 35B A3B
F0
35B24.5 GB3 tok/s4K ctx
moe
MistralMagistral Small 2507
F0
24B18.8 GB2 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B18.8 GB2 tok/s4K ctx
dense
AlibabaQwen 3 32B
F0
32B25.1 GB2 tok/s4K ctx
dense
AlibabaQwen 3 14B
F0
14B12.7 GB5 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B21.8 GB3 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B79.7 GB2 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B73.3 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B50.5 GB2 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B78.0 GB2 tok/s4K ctx
dense
NVIDIANemotron 3 Nano 30B
F0
30B22.4 GB2 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B52.0 GB2 tok/s4K ctx
moe
MicrosoftPhi-4-reasoning-plus 14B
F0
14.7B13.7 GB4 tok/s4K ctx
dense
MistralDevstral Small 1.1
F0
24B18.8 GB2 tok/s4K ctx
dense
Z.aiGLM-5.1
F0
754B480.7 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B82.7 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B474.6 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B411.5 GB2 tok/s4K ctx
moe
OpenAIGPT-OSS 20B
F0
21B17.0 GB4 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B147.9 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B297.4 GB2 tok/s4K ctx
moe
NVIDIANemotron Cascade 2 30B A3B
F0
30B22.9 GB3 tok/s4K ctx
moe
GoogleGemma 4 31B
F0
30.7B35.1 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B145.8 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B83.1 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B204.3 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B470.6 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B470.6 GB2 tok/s4K ctx
moe
MistralMinistral 3 14B
F0
14B12.7 GB5 tok/s4K ctx
multimodal
LG AIEXAONE 4.0 32B
F0
32B25.1 GB2 tok/s4K ctx
dense
GoogleGemma 4 26B A4B
F0
25.2B20.7 GB4 tok/s4K ctx
moe

Fast erreichbar

Modelle, die Sie mit einem Upgrade ausführen könnten

Hochwertige Modelle, die etwas mehr Speicher benötigen

1000BStufe 100Benötigt ca. 615.4 GB
1000BStufe 100Benötigt ca. 615.4 GB

Image & Video Generation

Diffusion Model Compatibility

21 of 52 models can generate images or video on your RX 6650 XT 8GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~2.8sS
Stable Diffusion 1.5Image512×768~5.6sS
Realistic Vision v5.1Image512×768~5.6sS
DreamShaper 8Image512×768~5.6sS
LCM DreamShaper v7Image512×768~1.7sS
PixArt-SigmaImage256×256~22.2sS
FramePack I2VVideo256×256~40.8s/frameA
SDXL TurboImage256×256~7.4sA
SDXL LightningImage256×256~22.1sB
Stable Diffusion XL 1.0Image256×256~59sB
Playground v2.5Image256×256~33.3sB
RealVisXL v5.0Image256×256~1m 6sB
DreamShaper XLImage256×256~1m 6sB
Juggernaut XL v9Image256×256~1m 6sB
Animagine XL 3.1Image256×256~1m 6sB
Pony Diffusion V6 XLImage256×256~1m 6sB
Animagine XL 4.0Image256×256~1m 6sB
Illustrious XLImage256×256~1m 6sB
Wan Video 2.1 1.3BVideo256×256~16.2s/frameD
Stable Diffusion 3.5 MediumImage256×256~38.9sD
Flux.2 Klein 4BImage256×256~6.7sD
LTX Video 2BVideo256×256~19.3s/frameF
KolorsImage256×256~44.4sF
Stable CascadeImage256×256~55.5sF
AuraFlow v0.3Image256×256~1m 40sF
Stable Diffusion 3.5 LargeImage256×256~2m 2sF
Stable Diffusion 3.5 Large TurboImage256×256~22.2sF
CogVideoX 2BVideo256×256~19.3s/frameF
HunyuanVideoVideo256×256~40.8s/frameF
ChromaImage256×256~22.2sF
Z-Image TurboImage256×256~22.9sF
Flux.1 DevImage256×256~1m 40sF
Flux.1 SchnellImage256×256~19.4sF
LTX Video 13BVideo256×256~40.8s/frameF
Flux.1 Kontext DevImage256×256~1m 51sF
AnimateDiff v1.5.3Video512×768~10.1s/frameF
Cosmos Diffusion 7BVideo256×256~31.8s/frameF
CogVideoX 5BVideo256×256~27.8s/frameF
Wan2.2 TI2V 5BVideo256×256~27.8s/frameF
Flux.2 Klein 9BImage256×256~11.1sF
Flux.1 Fill DevImage256×256~1m 34sF
Mochi 1 PreviewVideo256×256~36.7s/frameF
HunyuanVideo 1.5Video256×256~34.1s/frameF
Helios 14BVideo256×256~42s/frameF
SkyReels V2 14BVideo256×256~42s/frameF
Wan Video 2.1 14BVideo256×256~42s/frameF
Wan Video 2.2 14BVideo256×256~42s/frameF
Qwen ImageImage256×256~37.4sF
Qwen Image EditImage256×256~37.4sF
Flux.2 DevImage256×256~17m 31sF
MAGI-1Video256×256~52.1s/frameF
HunyuanImage 3.0Image256×256~1m 6sF

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 6650 XT 8GB

See what you unlock with more powerful hardware

Upgrade-Optionen

Upgrade-Optionen

Frequently Asked Questions

What AI models can I run on RX 6650 XT 8GB?

RX 6650 XT 8GB (8 GB VRAM) can run these top models: Qwen 3.5 4B (score: 95/100), Phi-4 Mini Reasoning 4B (score: 92/100), Jina Embeddings v3 (score: 84/100). See the full compatibility list above.

How much VRAM does RX 6650 XT 8GB have for AI?

RX 6650 XT 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is RX 6650 XT 8GB good for running LLMs locally?

Yes, RX 6650 XT 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RX 6650 XT 8GB for coding?

For coding on RX 6650 XT 8GB, we recommend Qwen 3.5 4B. It achieves 56.0 tokens per second with 28K 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 6650 XT 8GB?

There are 4 upgrade path(s) from RX 6650 XT 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.

Can RX 6650 XT 8GB run Flux for image generation?

Flux.1 Dev requires around 24 GB of usable memory at FP16. With 8 GB, RX 6650 XT 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.

What image and video AI models can I run on RX 6650 XT 8GB?

RX 6650 XT 8GB (8 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, video generation is limited by available memory. Check the AI Capability Matrix above for detailed compatibility.

Is RX 6650 XT 8GB good for AI image generation?

RX 6650 XT 8GB can handle basic AI image generation with SDXL and SD 1.5. With 8 GB of usable memory, larger models like Flux will need quantization or offloading. Best suited for standard resolution (512-1024px) generation.

Can RX 6650 XT 8GB run Qwen 3.5 27B?

Qwen 3.5 27B does not fit on RX 6650 XT 8GB with 8 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 6650 XT 8GB?

With 8 GB on RX 6650 XT 8GB, 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 6650 XT 8GB, does VRAM matter more than bandwidth?

On RX 6650 XT 8GB, 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.

Compare with similar