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 8GB is a budget Ampere card that offers just enough VRAM to run 7B models at FP16 — but barely. The 8 GB VRAM fits a 7B model in Q4 with some room for KV cache, while the 3rd-gen Tensor Cores with INT8 sparsity acceleration give it a meaningful edge over Turing-era cards. Memory bandwidth at 224 GB/s is its main weakness — token generation on a loaded 7B model will feel sluggish compared to even the RTX 3060 Ti. Good for first-time AI experimentation on a tight budget.
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.
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7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。
8.0 GB
VRAM
$249
希望小売価格
$31/GB
GBあたりのコスト
このGPUに最適なモデル
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.
もっと余裕が欲しいですか？ RTX 3080 10GB (10.0 GB VRAM) が次のステップアップです。
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
21 of 52 models can generate images or video on your RTX 3050 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~2.6s | S |
| Stable Diffusion 1.5Image | 512×768 | ~5.2s | S |
| Realistic Vision v5.1Image | 512×768 | ~5.2s | S |
| DreamShaper 8Image | 512×768 | ~5.2s | S |
| LCM DreamShaper v7Image | 512×768 | ~1.6s | S |
| PixArt-SigmaImage | 256×256 | ~21s | S |
| FramePack I2VVideo | 256×256 | ~38.5s/frame | A |
| SDXL TurboImage | 256×256 | ~7s | A |
| SDXL LightningImage | 256×256 | ~20.9s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~55.7s | B |
| Playground v2.5Image | 256×256 | ~31.5s | B |
| RealVisXL v5.0Image | 256×256 | ~1m 3s | B |
| DreamShaper XLImage | 256×256 | ~1m 3s | B |
| Juggernaut XL v9Image | 256×256 | ~1m 3s | B |
| Animagine XL 3.1Image | 256×256 | ~1m 3s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~1m 3s | B |
| Animagine XL 4.0Image | 256×256 | ~1m 3s | B |
| Illustrious XLImage | 256×256 | ~1m 3s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~15.3s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~36.7s | D |
| Flux.2 Klein 4BImage | 256×256 | ~6.3s | D |
| LTX Video 2BVideo | 256×256 | ~18.2s/frame | F |
| KolorsImage | 256×256 | ~42s | F |
| Stable CascadeImage | 256×256 | ~52.5s | F |
| AuraFlow v0.3Image | 256×256 | ~1m 34s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 55s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~21s | F |
| CogVideoX 2BVideo | 256×256 | ~18.2s/frame | F |
| HunyuanVideoVideo | 256×256 | ~38.5s/frame | F |
| ChromaImage | 256×256 | ~21s | F |
| Z-Image TurboImage | 256×256 | ~21.7s | F |
| Flux.1 DevImage | 256×256 | ~1m 34s | F |
| Flux.1 SchnellImage | 256×256 | ~18.4s | F |
| LTX Video 13BVideo | 256×256 | ~38.5s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~1m 45s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~9.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~30.1s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~26.3s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~26.3s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~10.5s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 29s | F |
| Mochi 1 PreviewVideo | 256×256 | ~34.7s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~32.2s/frame | F |
| Helios 14BVideo | 256×256 | ~39.7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~39.7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~39.7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~39.7s/frame | F |
| Qwen ImageImage | 256×256 | ~35.3s | F |
| Qwen Image EditImage | 256×256 | ~35.3s | F |
| Flux.2 DevImage | 256×256 | ~16m 33s | F |
| MAGI-1Video | 256×256 | ~49.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 2s | 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 3080 10GB次のステップUnlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 136%.
〜$699 MSRP
RTX 2080 Ti 11GBNVIDIAアップグレードUnlocks 34 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 136%.
〜$999 MSRP
Unlocks 74 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 13%.
〜$329 MSRP
Unlocks 155 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 487%.
〜$8,000 MSRP
RTX 3050 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 3050 8GB has 8 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 3050 8GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 3050 8GB, we recommend Qwen 3.5 4B. It achieves 48.0 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 3050 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 3050 8GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 3050 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 3050 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 3050 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 3050 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 3050 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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