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 3070 Ti 8GB is the higher-bandwidth sibling of the RTX 3070, pushing 608 GB/s via GDDR6X memory. For AI inference, this translates to faster decode speeds on 7B models compared to the standard 3070 (448 GB/s). Unfortunately, both share the 8 GB VRAM ceiling, which remains the limiting factor — faster generation of models that fit, but no access to larger model sizes. If you already own one, it's a capable 7B inference card; buying new, the RTX 3060 12GB offers more practical AI headroom.
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
Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.
AI Relevance
Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.
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 7 个，主要是较小的模型。较大模型需要强量化或无法适配。
8.0 GB
VRAM
$599
建议零售价
$75/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 33 additional models that do not fit on the current setup.
想要更多余量？ RTX 3080 10GB (10.0 GB VRAM) 是下一步升级选择。
Chat
SQwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Coding
ACodestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Agentic Coding
AGemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Reasoning
ACodestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
RAG
AGranite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Qwen 3.5 4B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3Qwen 3.5 9BQwen 3 8B
BGE M3Nemotron 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 A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
21 of 52 models can generate images or video on your RTX 3070 Ti 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1s | S |
| Stable Diffusion 1.5Image | 512×768 | ~2s | S |
| Realistic Vision v5.1Image | 512×768 | ~2s | S |
| DreamShaper 8Image | 512×768 | ~2s | S |
| LCM DreamShaper v7Image | 512×768 | 600ms | S |
| PixArt-SigmaImage | 256×256 | ~7.9s | S |
| FramePack I2VVideo | 256×256 | ~14.4s/frame | A |
| SDXL TurboImage | 256×256 | ~2.6s | A |
| SDXL LightningImage | 256×256 | ~7.8s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~20.9s | B |
| Playground v2.5Image | 256×256 | ~11.8s | B |
| RealVisXL v5.0Image | 256×256 | ~23.5s | B |
| DreamShaper XLImage | 256×256 | ~23.5s | B |
| Juggernaut XL v9Image | 256×256 | ~23.5s | B |
| Animagine XL 3.1Image | 256×256 | ~23.5s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~23.5s | B |
| Animagine XL 4.0Image | 256×256 | ~23.5s | B |
| Illustrious XLImage | 256×256 | ~23.5s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~5.8s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~13.8s | D |
| Flux.2 Klein 4BImage | 256×256 | ~2.4s | D |
| LTX Video 2BVideo | 256×256 | ~6.8s/frame | F |
| KolorsImage | 256×256 | ~15.7s | F |
| Stable CascadeImage | 256×256 | ~19.7s | F |
| AuraFlow v0.3Image | 256×256 | ~35.4s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~43.3s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~7.9s | F |
| CogVideoX 2BVideo | 256×256 | ~6.8s/frame | F |
| HunyuanVideoVideo | 256×256 | ~14.4s/frame | F |
| ChromaImage | 256×256 | ~7.9s | F |
| Z-Image TurboImage | 256×256 | ~8.1s | F |
| Flux.1 DevImage | 256×256 | ~35.4s | F |
| Flux.1 SchnellImage | 256×256 | ~6.9s | F |
| LTX Video 13BVideo | 256×256 | ~14.4s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~39.3s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~3.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~11.3s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~9.9s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~9.9s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~3.9s | F |
| Flux.1 Fill DevImage | 256×256 | ~33.4s | F |
| Mochi 1 PreviewVideo | 256×256 | ~13s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~12.1s/frame | F |
| Helios 14BVideo | 256×256 | ~14.9s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~14.9s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~14.9s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~14.9s/frame | F |
| Qwen ImageImage | 256×256 | ~13.2s | F |
| Qwen Image EditImage | 256×256 | ~13.2s | F |
| Flux.2 DevImage | 256×256 | ~6m 12s | F |
| MAGI-1Video | 256×256 | ~18.5s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~23.3s | 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
RTX 3080 10GB下一步升级Unlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 10%.
~$699 MSRP
RTX 2080 Ti 11GBNVIDIA 升级Unlocks 34 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 10%.
~$999 MSRP
Unlocks 74 additional models that do not fit on the current setup.
~$329 MSRP
Unlocks 155 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 174%.
~$8,000 MSRP
RTX 3070 Ti 8GB (8 GB VRAM) can run these top models: Qwen 3.5 4B (score: 95/100), Phi-4 Mini Reasoning 4B (score: 91/100), Jina Embeddings v3 (score: 83/100). See the full compatibility list above.
RTX 3070 Ti 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 3070 Ti 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 3070 Ti 8GB, we recommend Codestral Mamba 7B. It achieves 98.0 tokens per second with 67K context window. Codestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
There are 4 upgrade path(s) from RTX 3070 Ti 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 3070 Ti 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 3070 Ti 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 3070 Ti 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 3070 Ti 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 3070 Ti 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 3070 Ti 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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