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 4060 Ti 8GB is a competent Ada Lovelace mid-range card, offering 4th-gen Tensor Cores with FP8 support at a 160W TDP. Despite 44 TFLOPS of FP16 compute — matching the RTX 3070 Ti — the 288 GB/s bandwidth (128-bit bus) is the bottleneck for AI decode, similar to the RTX 4060 Ti 16GB. The 8 GB VRAM limits you to 7B models at Q4. For the same price, the RTX 4060 Ti 16GB is almost always the better AI buy if you can tolerate the same bandwidth constraint.
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
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
AI Relevance
FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.
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
$399
建议零售价
$50/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。
Cost vs cloud API
Assumes 4 hours/day of active inference at 64 tok/s, RTX 4060 Ti 8GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
27.6M
Tokens/month at this pace
$14.0
Monthly local cost
$276
Same tokens on cloud API
$0.505
Local $/1M tokens
Break-even: pays for itself in 1.5 months vs cloud API at this workload. Price reference: $399 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.
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 v3
BGE M3Qwen 3.5 9BQwen 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 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 4060 Ti 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1s | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.9s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.9s | S |
| DreamShaper 8Image | 512×768 | ~1.9s | S |
| LCM DreamShaper v7Image | 512×768 | 600ms | S |
| PixArt-SigmaImage | 256×256 | ~7.8s | S |
| FramePack I2VVideo | 256×256 | ~14.2s/frame | A |
| SDXL TurboImage | 256×256 | ~2.6s | A |
| SDXL LightningImage | 256×256 | ~7.7s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~20.6s | B |
| Playground v2.5Image | 256×256 | ~11.6s | B |
| RealVisXL v5.0Image | 256×256 | ~23.2s | B |
| DreamShaper XLImage | 256×256 | ~23.2s | B |
| Juggernaut XL v9Image | 256×256 | ~23.2s | B |
| Animagine XL 3.1Image | 256×256 | ~23.2s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~23.2s | B |
| Animagine XL 4.0Image | 256×256 | ~23.2s | B |
| Illustrious XLImage | 256×256 | ~23.2s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~5.7s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~13.6s | D |
| Flux.2 Klein 4BImage | 256×256 | ~2.3s | D |
| LTX Video 2BVideo | 256×256 | ~6.7s/frame | F |
| KolorsImage | 256×256 | ~15.5s | F |
| Stable CascadeImage | 256×256 | ~19.4s | F |
| AuraFlow v0.3Image | 256×256 | ~34.9s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~42.7s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~7.8s | F |
| CogVideoX 2BVideo | 256×256 | ~6.7s/frame | F |
| HunyuanVideoVideo | 256×256 | ~14.2s/frame | F |
| ChromaImage | 256×256 | ~7.8s | F |
| Z-Image TurboImage | 256×256 | ~8s | F |
| Flux.1 DevImage | 256×256 | ~34.9s | F |
| Flux.1 SchnellImage | 256×256 | ~6.8s | F |
| LTX Video 13BVideo | 256×256 | ~14.2s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~38.8s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~3.5s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~11.1s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~9.7s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~9.7s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~3.9s | F |
| Flux.1 Fill DevImage | 256×256 | ~33s | F |
| Mochi 1 PreviewVideo | 256×256 | ~12.8s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~11.9s/frame | F |
| Helios 14BVideo | 256×256 | ~14.7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~14.7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~14.7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~14.7s/frame | F |
| Qwen ImageImage | 256×256 | ~13.1s | F |
| Qwen Image EditImage | 256×256 | ~13.1s | F |
| Flux.2 DevImage | 256×256 | ~6m 7s | F |
| MAGI-1Video | 256×256 | ~18.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~23s | 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 60%.
~$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 60%.
~$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 298%.
~$8,000 MSRP
RTX 4060 Ti 8GB (8 GB VRAM) can run these top models: Qwen 3.5 4B (score: 96/100), Phi-4 Mini Reasoning 4B (score: 92/100), Jina Embeddings v3 (score: 84/100). See the full compatibility list above.
RTX 4060 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 4060 Ti 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 4060 Ti 8GB, we recommend Qwen 3.5 4B. It achieves 64.0 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 4060 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 4060 Ti 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 4060 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 4060 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 4060 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 4060 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 4060 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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