Will It Run AI

AMD

Radeon RX 7600M 8GB

Radeon RX 7000 MobileLaptopRDNA 3MOBILEROCm
8GB
VRAM
288GB/s
Bandwidth
21TFLOPS
FP16 Compute
168TOPS
INT8 Inference
VRAM8 GBBandwidth288 GB/sCompute21 TFInference168 TOPS
Radeon RX 7600M 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 Radeon RX 7600M 8GB is a mid-range RDNA 3 mobile GPU for gaming laptops. RDNA 3 has community ROCm support, and the 7600M may work with the HSA_OVERRIDE_GFX_VERSION approach on Linux, though it is not officially supported. With 8 GB of GDDR6 VRAM it can run 7B Q4 models. It's a capable secondary use of a gaming laptop's GPU for light AI inference.

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
rocm-experimentallaptopsoftware-limited

Especificaciones

Cómputo
FP1621 TFLOPS
INT8168 TOPS
ArquitecturaRDNA 3
Memoria
VRAM8 GB
Ancho de banda288 GB/s
General
FamiliaRadeon RX 7000 Mobile
SegmentoLaptop
InterconexiónMOBILE
Plataforma de cómputoROCM

Características clave

RDNA 3 architecture (Navi 33 mobile)8 GB GDDR6 on a 128-bit bus288 GB/s memory bandwidth32 Compute UnitsMobile interconnectCommunity ROCm support possible — not officially listed

Para cargas de trabajo de IA

Fortalezas
  • RDNA 3 enables community ROCm experimentation — improvement over RDNA 2 laptops
  • 8 GB fits 7B Q4 models for light local inference
  • llama.cpp Vulkan works without ROCm configuration
  • Laptop form factor for portable AI inference
Consideraciones
  • Not officially ROCm supported — community workarounds needed
  • 8 GB is a tight limit for modern LLMs
  • Mobile thermal constraints reduce sustained performance
  • ROCm community support for mobile GPUs is less tested than desktop variants

Architecture

RDNA 3

RDNA 3 is AMD's chiplet-based GPU architecture, combining a 5nm Graphics Compute Die (GCD) with 6nm Memory Cache Dies (MCDs). It introduces AI accelerators and a new unified compute unit design.

AI Relevance

ROCm support for RDNA 3 is maturing but lags behind NVIDIA's CUDA ecosystem. AI accelerator units provide some inference acceleration, but lack the dedicated Tensor Core equivalent found in NVIDIA GPUs.

Process: TSMC 5nm + 6nmPlatform: ROCMPrecisions: FP32, FP16, BF16, INT8

Consejo de compra

¿Deberías comprar Radeon RX 7600M 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

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 45.0 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 GB18 tok/s6K ctx
dense
AlibabaQwen 3 8B
A79
8B8.8 GB23 tok/s10K ctx
dense
NVIDIANemotron Nano 8B
A75
8B8.5 GB25 tok/s12K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.8 GB4 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 GB4 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 GB4 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 GB6 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B21.8 GB4 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 GB5 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 GB5 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 GB4 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 GB6 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

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 Radeon RX 7600M 8GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~2.5sS
Stable Diffusion 1.5Image512×768~5sS
Realistic Vision v5.1Image512×768~5sS
DreamShaper 8Image512×768~5sS
LCM DreamShaper v7Image512×768~1.5sS
PixArt-SigmaImage256×256~20.1sS
FramePack I2VVideo256×256~36.9s/frameA
SDXL TurboImage256×256~6.7sA
SDXL LightningImage256×256~20sB
Stable Diffusion XL 1.0Image256×256~53.4sB
Playground v2.5Image256×256~30.2sB
RealVisXL v5.0Image256×256~1m 0sB
DreamShaper XLImage256×256~1m 0sB
Juggernaut XL v9Image256×256~1m 0sB
Animagine XL 3.1Image256×256~1m 0sB
Pony Diffusion V6 XLImage256×256~1m 0sB
Animagine XL 4.0Image256×256~1m 0sB
Illustrious XLImage256×256~1m 0sB
Wan Video 2.1 1.3BVideo256×256~14.7s/frameD
Stable Diffusion 3.5 MediumImage256×256~35.2sD
Flux.2 Klein 4BImage256×256~6sD
LTX Video 2BVideo256×256~17.5s/frameF
KolorsImage256×256~40.2sF
Stable CascadeImage256×256~50.3sF
AuraFlow v0.3Image256×256~1m 31sF
Stable Diffusion 3.5 LargeImage256×256~1m 51sF
Stable Diffusion 3.5 Large TurboImage256×256~20.1sF
CogVideoX 2BVideo256×256~17.5s/frameF
HunyuanVideoVideo256×256~36.9s/frameF
ChromaImage256×256~20.1sF
Z-Image TurboImage256×256~20.8sF
Flux.1 DevImage256×256~1m 31sF
Flux.1 SchnellImage256×256~17.6sF
LTX Video 13BVideo256×256~36.9s/frameF
Flux.1 Kontext DevImage256×256~1m 41sF
AnimateDiff v1.5.3Video512×768~9.2s/frameF
Cosmos Diffusion 7BVideo256×256~28.8s/frameF
CogVideoX 5BVideo256×256~25.2s/frameF
Wan2.2 TI2V 5BVideo256×256~25.2s/frameF
Flux.2 Klein 9BImage256×256~10.1sF
Flux.1 Fill DevImage256×256~1m 26sF
Mochi 1 PreviewVideo256×256~33.3s/frameF
HunyuanVideo 1.5Video256×256~30.9s/frameF
Helios 14BVideo256×256~38s/frameF
SkyReels V2 14BVideo256×256~38s/frameF
Wan Video 2.1 14BVideo256×256~38s/frameF
Wan Video 2.2 14BVideo256×256~38s/frameF
Qwen ImageImage256×256~33.9sF
Qwen Image EditImage256×256~33.9sF
Flux.2 DevImage256×256~15m 52sF
MAGI-1Video256×256~47.2s/frameF
HunyuanImage 3.0Image256×256~59.7sF

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 Radeon RX 7600M 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 Radeon RX 7600M 8GB?

Radeon RX 7600M 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 Radeon RX 7600M 8GB have for AI?

Radeon RX 7600M 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is Radeon RX 7600M 8GB good for running LLMs locally?

Yes, Radeon RX 7600M 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for Radeon RX 7600M 8GB for coding?

For coding on Radeon RX 7600M 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 Radeon RX 7600M 8GB?

There are 4 upgrade path(s) from Radeon RX 7600M 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.

Can Radeon RX 7600M 8GB run Flux for image generation?

Flux.1 Dev requires around 24 GB of usable memory at FP16. With 8 GB, Radeon RX 7600M 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 Radeon RX 7600M 8GB?

Radeon RX 7600M 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 Radeon RX 7600M 8GB good for AI image generation?

Radeon RX 7600M 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 Radeon RX 7600M 8GB run Qwen 3.5 27B?

Qwen 3.5 27B does not fit on Radeon RX 7600M 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 Radeon RX 7600M 8GB?

With 8 GB on Radeon RX 7600M 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 Radeon RX 7600M 8GB, does VRAM matter more than bandwidth?

On Radeon RX 7600M 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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