NVIDIA
Operating mode
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.
The RTX 5060 8GB is NVIDIA's entry-level Blackwell consumer GPU, bringing FP4/FP8 Tensor Core support to a $299 price point. The 8 GB GDDR7 VRAM with 448 GB/s bandwidth handles 7B models well, and FP4 support means you can squeeze higher-quality quantized inference into that 8 GB than any prior-gen 8 GB card could manage. The 150W TDP makes it an excellent always-on AI server card. That said, 8 GB is still 8 GB — 13B models are out of reach without accepting significant quality loss.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 |
| Video Short (25f) | Won't fit | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
Consejo de compra
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
$299
PVP
$37/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.
Chat
SThis 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.
Coding
SThis 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.
Agentic Coding
AThis 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.
Reasoning
SThis 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.
RAG
AThis 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.
Qwen 3.5 4B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3Qwen 3.5 9B
BGE M3Qwen 3 8BNemotron Nano 8BQwen3-Coder 30B A3B InstructQwen 3.5 397B A17B
Devstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 27BQwen 3.6 27BQwen 3.5 122B A10BQwen3-VL 30B A3B InstructQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 35B A3BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 32BQwen 3 14BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextPhi-4-reasoning-plus 14BDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2GPT-OSS 20BQwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BMinistral 3 14B
EXAONE 4.0 32BGemma 4 26B A4BCasi al alcance
Modelos de alta calidad que necesitan un poco más de memoria
Image & Video Generation
21 of 52 models can generate images or video on your RTX 5060 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1.3s | S |
| Stable Diffusion 1.5Image | 512×768 | ~2.7s | S |
| Realistic Vision v5.1Image | 512×768 | ~2.7s | S |
| DreamShaper 8Image | 512×768 | ~2.7s | S |
| LCM DreamShaper v7Image | 512×768 | 800ms | S |
| PixArt-SigmaImage | 256×256 | ~10.8s | S |
| FramePack I2VVideo | 256×256 | ~19.7s/frame | A |
| SDXL TurboImage | 256×256 | ~3.6s | A |
| SDXL LightningImage | 256×256 | ~10.7s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~28.5s | B |
| Playground v2.5Image | 256×256 | ~16.1s | B |
| RealVisXL v5.0Image | 256×256 | ~32.1s | B |
| DreamShaper XLImage | 256×256 | ~32.1s | B |
| Juggernaut XL v9Image | 256×256 | ~32.1s | B |
| Animagine XL 3.1Image | 256×256 | ~32.1s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~32.1s | B |
| Animagine XL 4.0Image | 256×256 | ~32.1s | B |
| Illustrious XLImage | 256×256 | ~32.1s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~7.9s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~18.8s | D |
| Flux.2 Klein 4BImage | 256×256 | ~3.2s | D |
| LTX Video 2BVideo | 256×256 | ~9.3s/frame | F |
| KolorsImage | 256×256 | ~21.5s | F |
| Stable CascadeImage | 256×256 | ~26.9s | F |
| AuraFlow v0.3Image | 256×256 | ~48.4s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~59.1s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~10.8s | F |
| CogVideoX 2BVideo | 256×256 | ~9.3s/frame | F |
| HunyuanVideoVideo | 256×256 | ~19.7s/frame | F |
| ChromaImage | 256×256 | ~10.8s | F |
| Z-Image TurboImage | 256×256 | ~11.1s | F |
| Flux.1 DevImage | 256×256 | ~48.4s | F |
| Flux.1 SchnellImage | 256×256 | ~9.4s | F |
| LTX Video 13BVideo | 256×256 | ~19.7s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~53.8s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~4.9s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~15.4s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~13.5s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~13.5s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~5.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~45.7s | F |
| Mochi 1 PreviewVideo | 256×256 | ~17.8s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~16.5s/frame | F |
| Helios 14BVideo | 256×256 | ~20.3s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~20.3s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~20.3s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~20.3s/frame | F |
| Qwen ImageImage | 256×256 | ~18.1s | F |
| Qwen Image EditImage | 256×256 | ~18.1s | F |
| Flux.2 DevImage | 256×256 | ~8m 29s | F |
| MAGI-1Video | 256×256 | ~25.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~31.9s | F |
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
See what you unlock with more powerful hardware
Opciones de mejora
RTX 3080 10GBSiguiente nivelDesbloquea 33 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 20% en los modelos que sí caben.
~$699 MSRP
RTX 2080 Ti 11GBMejora NVIDIADesbloquea 34 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 20% en los modelos que sí caben.
~$999 MSRP
Desbloquea 74 modelos adicionales que hoy no caben en tu setup.
~$329 MSRP
Desbloquea 155 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 198% en los modelos que sí caben.
~$8,000 MSRP
RTX 5060 8GB (8 GB VRAM) can run these top models: Qwen 3.5 4B (score: 96/100), Phi-4 Mini Reasoning 4B (score: 93/100), Jina Embeddings v3 (score: 85/100). See the full compatibility list above.
RTX 5060 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 5060 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 5060 8GB, we recommend Qwen 3.5 4B. It achieves 76.0 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.
There are 4 upgrade path(s) from RTX 5060 8GB: RTX 3080 10GB, RTX 2080 Ti 11GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 8 GB, RTX 5060 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 5060 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.
RTX 5060 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.
Qwen 3.5 27B does not fit on RTX 5060 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.
With 8 GB on RTX 5060 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.
On RTX 5060 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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