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 5070 12GB is NVIDIA's mid-range Blackwell consumer GPU, introducing GDDR7 memory and 5th-gen Tensor Cores with FP4 support to the $549 price point. The 672 GB/s bandwidth is a big improvement over similarly-priced Ada cards, and FP4 support unlocks a new level of memory efficiency — models that previously required Q4 can now run at higher quality in the same VRAM footprint. The 12 GB VRAM ceiling still limits you to 13B models and below, but within that envelope Blackwell's efficiency is genuinely better than Ada.
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 natively | 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) | Runs with offload | 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.
购买建议
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。
Cost vs cloud API
Assumes 4 hours/day of active inference at 83 tok/s, RTX 5070 12GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
35.8M
Tokens/month at this pace
$19.2
Monthly local cost
$358
Same tokens on cloud API
$0.536
Local $/1M tokens
Break-even: pays for itself in 1.6 months vs cloud API at this workload. Price reference: $549 MSRP.
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, lm-studio.
RAG
AThis 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.
Qwen 3.5 9BQwen 3 8BQwen 3.5 4BNemotron Nano 8B
Phi-4 Mini Reasoning 4BQwen 3 14B
Jina Embeddings v3
BGE M3
Ministral 3 14BPhi-4-reasoning-plus 14BQwen3-Coder 30B A3B InstructQwen 3.5 397B A17BDevstral 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 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextDevstral 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 671B
EXAONE 4.0 32BGemma 4 26B A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
24 of 52 models can generate images or video on your RTX 5070 12GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1.6s | S |
| Stable Diffusion 1.5Image | 512×768 | ~3.2s | S |
| Realistic Vision v5.1Image | 512×768 | ~3.2s | S |
| DreamShaper 8Image | 512×768 | ~3.2s | S |
| LCM DreamShaper v7Image | 512×768 | ~1s | S |
| PixArt-SigmaImage | 256×256 | ~57.4s | S |
| FramePack I2VVideo | 256×256 | ~23.4s/frame | S |
| SDXL TurboImage | 512×512 | ~1.6s | S |
| SDXL LightningImage | 1024×1024 | ~4.8s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~12.8s | S |
| Playground v2.5Image | 1024×1024 | ~19.1s | S |
| RealVisXL v5.0Image | 1024×1024 | ~14.4s | S |
| DreamShaper XLImage | 1024×1024 | ~14.4s | S |
| Juggernaut XL v9Image | 1024×1024 | ~14.4s | S |
| Animagine XL 3.1Image | 1024×1024 | ~14.4s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~14.4s | S |
| Animagine XL 4.0Image | 1024×1024 | ~14.4s | S |
| Illustrious XLImage | 1024×1024 | ~14.4s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~9.3s/frame | A |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~22.3s | A |
| Flux.2 Klein 4BImage | 256×256 | ~8.6s | A |
| LTX Video 2BVideo | 256×256 | ~11.1s/frame | B |
| KolorsImage | 256×256 | ~25.5s | B |
| Stable CascadeImage | 1024×1024 | ~31.9s | D |
| AuraFlow v0.3Image | 256×256 | ~57.4s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 10s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~12.8s | F |
| CogVideoX 2BVideo | 256×256 | ~11.1s/frame | F |
| HunyuanVideoVideo | 256×256 | ~23.4s/frame | F |
| ChromaImage | 256×256 | ~12.8s | F |
| Z-Image TurboImage | 256×256 | ~13.2s | F |
| Flux.1 DevImage | 256×256 | ~57.4s | F |
| Flux.1 SchnellImage | 256×256 | ~11.2s | F |
| LTX Video 13BVideo | 256×256 | ~23.4s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~1m 4s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~5.8s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~18.3s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~16s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~16s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~6.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~54.2s | F |
| Mochi 1 PreviewVideo | 256×256 | ~21.1s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~19.6s/frame | F |
| Helios 14BVideo | 256×256 | ~24.1s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~24.1s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~24.1s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~24.1s/frame | F |
| Qwen ImageImage | 256×256 | ~21.5s | F |
| Qwen Image EditImage | 256×256 | ~21.5s | F |
| Flux.2 DevImage | 256×256 | ~10m 4s | F |
| MAGI-1Video | 256×256 | ~29.9s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~37.8s | 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
升级选项
Unlocks 1 additional models that do not fit on the current setup.
~$1,999 MSRP
RTX 4070 Ti Super 16GBNVIDIA 升级Unlocks 37 additional models that do not fit on the current setup.
~$799 MSRP
Intel Arc Pro B60 24GB最佳性价比Unlocks 73 additional models that do not fit on the current setup.
~$599 MSRP
Unlocks 118 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 119%.
~$8,000 MSRP
RTX 5070 12GB (12 GB VRAM) can run these top models: Qwen 3.5 9B (score: 98/100), Qwen 3 8B (score: 97/100), Qwen 3.5 4B (score: 93/100). See the full compatibility list above.
RTX 5070 12GB has 12 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 5070 12GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 5070 12GB, we recommend Qwen 3.5 9B. It achieves 82.9 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.
There are 4 upgrade path(s) from RTX 5070 12GB: MacBook Pro M3 Pro 18GB, RTX 4070 Ti Super 16GB. Upgrading would unlock larger models and faster inference speeds.
RTX 5070 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.
RTX 5070 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.
RTX 5070 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.
Qwen 3.5 27B does not fit on RTX 5070 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.
With 12 GB on RTX 5070 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.
On RTX 5070 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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