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 3050 Ti Laptop 4GB is an Ampere mobile GPU in a highly constrained form factor. With only 4 GB of VRAM, it can run 1B–3B models on-GPU and handles some 7B models at Q2/Q3 if you're willing to accept heavy quantization and partial CPU offloading. The Ampere architecture with 3rd-gen Tensor Cores gives it efficiency advantages over similarly-VRAM-constrained Pascal cards, but 4 GB is simply too little for practical modern LLM use. Its main value is as an emergency compute resource in a laptop that won't otherwise have AI capability.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Won’t fit | 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) | Won't fit | 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.
購入アドバイス
制限付きでローカルAIに使用可能
上位50モデル中2モデルを実行可能（主に小規模）。大規模モデルには強い量子化が必要か、適合しません。
4.0 GB
VRAM
このGPUに最適なモデル
What will limit you first
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 6.8 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Best upgrade itinerary
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Unlocks 93 additional models that do not fit on the current setup.
もっと余裕が欲しいですか？ RTX 2060 6GB (6.0 GB VRAM) が次のステップアップです。
Chat
AThis 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
BThis model is still usable for coding, 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, lm-studio.
Agentic Coding
FThis 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 is likely to require compromise or offload. Known channels: huggingface, ollama, lm-studio.
Reasoning
BThis model is a direct match for reasoning. It sits in the middle of the current model mix. 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, lm-studio.
BGE M3
Jina Embeddings v3
Qwen3-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 9BQwen 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 30BQwen 3.5 4BQwen 3 8BQwen3-Coder-Next
Phi-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 A3BPhi-4 Mini Reasoning 4B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BNemotron Nano 8BMinistral 3 14B
EXAONE 4.0 32BGemma 4 26B A4Bもう少しで届く
もう少しメモリがあれば動く高品質モデル
Image & Video Generation
1 of 52 models can generate images or video on your RTX 3050 Ti Laptop 4GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~2.3s | D |
| Stable Diffusion 1.5Image | 512×768 | ~4.7s | F |
| Realistic Vision v5.1Image | 512×768 | ~4.7s | F |
| DreamShaper 8Image | 512×768 | ~4.7s | F |
| LCM DreamShaper v7Image | 512×768 | ~1.4s | F |
| PixArt-SigmaImage | 256×256 | ~18.8s | F |
| FramePack I2VVideo | 256×256 | ~34.5s/frame | F |
| SDXL TurboImage | 256×256 | ~2.3s | F |
| SDXL LightningImage | 256×256 | ~7s | F |
| Stable Diffusion XL 1.0Image | 256×256 | ~18.8s | F |
| Playground v2.5Image | 256×256 | ~28.2s | F |
| RealVisXL v5.0Image | 256×256 | ~21.1s | F |
| DreamShaper XLImage | 256×256 | ~21.1s | F |
| Juggernaut XL v9Image | 256×256 | ~21.1s | F |
| Animagine XL 3.1Image | 256×256 | ~21.1s | F |
| Pony Diffusion V6 XLImage | 256×256 | ~21.1s | F |
| Animagine XL 4.0Image | 256×256 | ~21.1s | F |
| Illustrious XLImage | 256×256 | ~21.1s | F |
| Wan Video 2.1 1.3BVideo | 256×256 | ~13.7s/frame | F |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~32.9s | F |
| Flux.2 Klein 4BImage | 256×256 | ~5.6s | F |
| LTX Video 2BVideo | 256×256 | ~16.3s/frame | F |
| KolorsImage | 256×256 | ~37.6s | F |
| Stable CascadeImage | 256×256 | ~47s | F |
| AuraFlow v0.3Image | 256×256 | ~1m 25s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 43s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~18.8s | F |
| CogVideoX 2BVideo | 256×256 | ~16.3s/frame | F |
| HunyuanVideoVideo | 256×256 | ~34.5s/frame | F |
| ChromaImage | 256×256 | ~18.8s | F |
| Z-Image TurboImage | 256×256 | ~19.4s | F |
| Flux.1 DevImage | 256×256 | ~1m 25s | F |
| Flux.1 SchnellImage | 256×256 | ~16.4s | F |
| LTX Video 13BVideo | 256×256 | ~34.5s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~1m 34s | F |
| AnimateDiff v1.5.3Video | 512×512 | ~8.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~26.9s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~23.5s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~23.5s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~9.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 20s | F |
| Mochi 1 PreviewVideo | 256×256 | ~31.1s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~28.8s/frame | F |
| Helios 14BVideo | 256×256 | ~35.5s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~35.5s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~35.5s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~35.5s/frame | F |
| Qwen ImageImage | 256×256 | ~31.6s | F |
| Qwen Image EditImage | 256×256 | ~31.6s | F |
| Flux.2 DevImage | 256×256 | ~14m 49s | F |
| MAGI-1Video | 256×256 | ~44.1s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~55.7s | 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
アップグレードオプション
RTX 2060 6GB次のステップUnlocks 93 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 30%.
〜$349 MSRP
GTX 1060 6GBNVIDIAアップグレードUnlocks 93 additional models that do not fit on the current setup.
〜$249 MSRP
Intel Arc B570 10GBコスパ最良Unlocks 164 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 63%.
〜$219 MSRP
Unlocks 286 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 583%.
〜$8,000 MSRP
RTX 3050 Ti Laptop 4GB (4 GB VRAM) can run these top models: BGE M3 (score: 82/100), Jina Embeddings v3 (score: 73/100), Qwen3-Coder 30B A3B Instruct (score: 0/100). See the full compatibility list above.
RTX 3050 Ti Laptop 4GB has 4 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 3050 Ti Laptop 4GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 3050 Ti Laptop 4GB, we recommend Qwen 2.5 Coder 1.5B. It achieves 18.0 tokens per second with 33K context window. This model is still usable for coding, 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, lm-studio.
There are 4 upgrade path(s) from RTX 3050 Ti Laptop 4GB: RTX 2060 6GB, GTX 1060 6GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 4 GB, RTX 3050 Ti Laptop 4GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 3050 Ti Laptop 4GB (4 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, Stable Diffusion 1.5 fits comfortably. For video, video generation is limited by available memory. Check the AI Capability Matrix above for detailed compatibility.
RTX 3050 Ti Laptop 4GB has limited capability for AI image generation with only 4 GB of usable memory. Stick to SD 1.5 at lower resolutions. For a better experience, consider hardware with at least 8 GB of usable accelerator memory.
Qwen 3.5 27B requires at least 16 GB of usable memory at Q4. With 4 GB, RTX 3050 Ti Laptop 4GB can run the 4B variant at Q4 (2.4 GB). Consider upgrading memory capacity for larger Qwen models.
With 4 GB on RTX 3050 Ti Laptop 4GB, stick to Q4_K_M for the best quality-to-size ratio. Only use Q2-Q3 if you must fit a model that otherwise would not load.
On RTX 3050 Ti Laptop 4GB, 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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