Will It Run AI

AMD

RX 5700 XT 8GB

RX 5000ConsumerRDNA 1PCIe 4ROCm
8GB
VRAM
448GB/s
Bandwidth
19TFLOPS
FP16 Compute
38TOPS
INT8 Inference
$399 MSRP
VRAM8 GBBandwidth448 GB/sCompute19 TFInference38 TOPSValue4.76 TF/$k
RX 5700 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 5700 XT 8GB was a popular mid-range card in 2019-2020 and remains available on the used market. RDNA 1 cards have no official ROCm support, so AI inference depends on Vulkan backends. The 8 GB of GDDR6 VRAM allows 7B models at Q4 quantization, which provides basic local LLM capability. However, the software limitations make it a poor choice compared to any RDNA 3 card in the same price range today.

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-onlylegacy

Especificaciones

Cómputo
FP1619 TFLOPS
INT838 TOPS
ArquitecturaRDNA 1
Memoria
VRAM8 GB
Ancho de banda448 GB/s
General
FamiliaRX 5000
SegmentoConsumer
InterconexiónPCIe 4
Plataforma de cómputoROCM
MSRP$399

Características clave

RDNA 1 architecture (Navi 10 die, high-end configuration)8 GB GDDR6 on a 256-bit bus448 GB/s memory bandwidth40 Compute UnitsPCIe Gen 4 x16No ROCm support — Vulkan inference only

Para cargas de trabajo de IA

Fortalezas
  • 8 GB VRAM fits 7B Q4 models with room for context
  • llama.cpp Vulkan backend runs reliably
  • Good used market pricing for RDNA 1
  • Reasonable memory bandwidth for its era
Consideraciones
  • No ROCm support — RDNA 1 is legacy and excluded from AMD's ecosystem
  • Vulkan is the only accelerated inference path
  • RDNA 3 cards like the RX 7600 XT 16GB offer more VRAM and community ROCm for less
  • Limited future software support for older architectures

Architecture

RDNA 1

RDNA 1 is AMD's first RDNA architecture, replacing the GCN design for consumer GPUs. Built on TSMC 7nm, it delivered significant IPC improvements over GCN 5 (Vega).

AI Relevance

Very limited AI inference support. No official ROCm support for consumer RDNA 1 cards. Vulkan-based backends in llama.cpp can work but with poor performance. Not recommended for AI workloads.

Process: TSMC 7nmPlatform: ROCMPrecisions: FP32, FP16

Consejo de compra

¿Deberías comprar RX 5700 XT 8GB para IA local?

Usable para IA local con limitaciones

Puede ejecutar 7 de 50 modelos principales, mayormente los más pequeños. Los modelos más grandes necesitan cuantización fuerte o no cabrán.

8.0 GB

VRAM

$399

PVP

$50/GB

Coste por GB de VRAM

Mejores modelos para esta GPU

What will limit you first

Este setup está bastante equilibrado para este modelo.

No hay grandes señales de alerta

Esta recomendación tiene margen de memoria suficiente y una velocidad estimada razonable para la carga de trabajo seleccionada.

Best upgrade itinerary

Desbloquea 33 modelos adicionales que hoy no caben en tu setup.

¿Quieres más margen? RTX 3080 10GB (10.0 GB VRAM) es el siguiente paso.

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 61.7 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
AlibabaQwen 3.5 9B
A81
9B9.4 GB24 tok/s6K ctx
dense
BAAIBGE M3
A81
0.57B4.0 GB8 tok/s8K ctx
dense
AlibabaQwen 3 8B
A80
8B8.8 GB32 tok/s10K ctx
dense
NVIDIANemotron Nano 8B
A76
8B8.5 GB34 tok/s12K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.8 GB5 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 GB6 tok/s4K ctx
moe
AlibabaQwen 3.6 35B A3B
F0
35B27.2 GB4 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Flash
F0
284B161.0 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 35B A3B
F0
35B24.5 GB5 tok/s4K ctx
moe
MistralMagistral Small 2507
F0
24B18.8 GB3 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B18.8 GB3 tok/s4K ctx
dense
AlibabaQwen 3 32B
F0
32B25.1 GB2 tok/s4K ctx
dense
AlibabaQwen 3 14B
F0
14B12.7 GB8 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B21.8 GB5 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 GB7 tok/s4K ctx
dense
MistralDevstral Small 1.1
F0
24B18.8 GB3 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 GB7 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 GB5 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 GB8 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 GB6 tok/s4K ctx
moe

Casi al alcance

Modelos que podrías ejecutar con una mejora

Modelos de alta calidad que necesitan un poco más de memoria

Image & Video Generation

Diffusion Model Compatibility

21 of 52 models can generate images or video on your RX 5700 XT 8GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~3.2sS
Stable Diffusion 1.5Image512×768~6.3sS
Realistic Vision v5.1Image512×768~6.3sS
DreamShaper 8Image512×768~6.3sS
LCM DreamShaper v7Image512×768~1.9sS
PixArt-SigmaImage256×256~25.2sS
FramePack I2VVideo256×256~46.3s/frameA
SDXL TurboImage256×256~8.4sA
SDXL LightningImage256×256~25.1sB
Stable Diffusion XL 1.0Image256×256~1m 7sB
Playground v2.5Image256×256~37.8sB
RealVisXL v5.0Image256×256~1m 15sB
DreamShaper XLImage256×256~1m 15sB
Juggernaut XL v9Image256×256~1m 15sB
Animagine XL 3.1Image256×256~1m 15sB
Pony Diffusion V6 XLImage256×256~1m 15sB
Animagine XL 4.0Image256×256~1m 15sB
Illustrious XLImage256×256~1m 15sB
Wan Video 2.1 1.3BVideo256×256~18.4s/frameD
Stable Diffusion 3.5 MediumImage256×256~44.2sD
Flux.2 Klein 4BImage256×256~7.6sD
LTX Video 2BVideo256×256~21.9s/frameF
KolorsImage256×256~50.5sF
Stable CascadeImage256×256~1m 3sF
AuraFlow v0.3Image256×256~1m 54sF
Stable Diffusion 3.5 LargeImage256×256~2m 19sF
Stable Diffusion 3.5 Large TurboImage256×256~25.2sF
CogVideoX 2BVideo256×256~21.9s/frameF
HunyuanVideoVideo256×256~46.3s/frameF
ChromaImage256×256~25.2sF
Z-Image TurboImage256×256~26sF
Flux.1 DevImage256×256~1m 54sF
Flux.1 SchnellImage256×256~22.1sF
LTX Video 13BVideo256×256~46.3s/frameF
Flux.1 Kontext DevImage256×256~2m 6sF
AnimateDiff v1.5.3Video512×768~11.5s/frameF
Cosmos Diffusion 7BVideo256×256~36.2s/frameF
CogVideoX 5BVideo256×256~31.6s/frameF
Wan2.2 TI2V 5BVideo256×256~31.6s/frameF
Flux.2 Klein 9BImage256×256~12.6sF
Flux.1 Fill DevImage256×256~1m 47sF
Mochi 1 PreviewVideo256×256~41.7s/frameF
HunyuanVideo 1.5Video256×256~38.7s/frameF
Helios 14BVideo256×256~47.7s/frameF
SkyReels V2 14BVideo256×256~47.7s/frameF
Wan Video 2.1 14BVideo256×256~47.7s/frameF
Wan Video 2.2 14BVideo256×256~47.7s/frameF
Qwen ImageImage256×256~42.5sF
Qwen Image EditImage256×256~42.5sF
Flux.2 DevImage256×256~19m 54sF
MAGI-1Video256×256~59.2s/frameF
HunyuanImage 3.0Image256×256~1m 15sF

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 5700 XT 8GB

See what you unlock with more powerful hardware

Opciones de mejora

Opciones de mejora

Frequently Asked Questions

What AI models can I run on RX 5700 XT 8GB?

RX 5700 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 5700 XT 8GB have for AI?

RX 5700 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 5700 XT 8GB good for running LLMs locally?

Yes, RX 5700 XT 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RX 5700 XT 8GB for coding?

For coding on RX 5700 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 5700 XT 8GB?

There are 4 upgrade path(s) from RX 5700 XT 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.

Can RX 5700 XT 8GB run Flux for image generation?

Flux.1 Dev requires around 24 GB of usable memory at FP16. With 8 GB, RX 5700 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 5700 XT 8GB?

RX 5700 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 5700 XT 8GB good for AI image generation?

RX 5700 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 5700 XT 8GB run Qwen 3.5 27B?

Qwen 3.5 27B does not fit on RX 5700 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 5700 XT 8GB?

With 8 GB on RX 5700 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 5700 XT 8GB, does VRAM matter more than bandwidth?

On RX 5700 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.

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