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 GTX 1060 6GB is a Pascal-era card with no Tensor Cores and CUDA compute capability 6.1 — at the edge of what's still practical for local AI. With 6 GB of VRAM, 7B models require aggressive Q3/Q4 quantization to fit, and generation is slow since all compute runs on CUDA cores without any INT8 acceleration. This GPU is running on borrowed time: NVIDIA has announced Pascal support will be dropped in future CUDA releases (post-12.x), which will progressively break compatibility with new LLM frameworks. Use it if you already own it, but don't buy one for AI.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Needs offload | 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) | Very constrained | 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
Pascal is NVIDIA's first 16nm FinFET GPU architecture, powering the GTX 10-series consumer cards and Tesla P100/P40 datacenter accelerators. It introduced unified memory architecture and NVLink interconnect for datacenter GPUs.
AI Relevance
No dedicated Tensor Cores — all AI inference runs on standard CUDA cores at FP16 or FP32 precision. Still usable for small models (7B Q4) on cards with sufficient VRAM like the GTX 1080 Ti (11 GB) or P40 (24 GB), but significantly slower than Turing and newer.
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4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。
6.0 GB
VRAM
$249
希望小売価格
$42/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.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Unlocks 38 additional models that do not fit on the current setup.
もっと余裕が欲しいですか？ RTX 3050 8GB (8.0 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.
Coding
AThis model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. 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 should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
Reasoning
AThis model is a direct match for reasoning. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
RAG
BThis model is a direct match for rag. It sits in the middle of the current model mix. It is likely to require compromise or offload. Known channels: huggingface, ollama.
Qwen 3.5 4B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3
BGE M3Qwen3-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 8BQwen3-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 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
18 of 52 models can generate images or video on your GTX 1060 6GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~6.6s | A |
| Stable Diffusion 1.5Image | 512×768 | ~13.2s | B |
| Realistic Vision v5.1Image | 512×768 | ~13.2s | B |
| DreamShaper 8Image | 512×768 | ~13.2s | B |
| LCM DreamShaper v7Image | 512×768 | ~4s | B |
| PixArt-SigmaImage | 256×256 | ~52.8s | B |
| FramePack I2VVideo | 256×256 | ~1m 37s/frame | B |
| SDXL TurboImage | 256×256 | ~6.6s | D |
| SDXL LightningImage | 256×256 | ~19.8s | D |
| Stable Diffusion XL 1.0Image | 256×256 | ~52.8s | D |
| Playground v2.5Image | 256×256 | ~1m 19s | D |
| RealVisXL v5.0Image | 256×256 | ~59.4s | D |
| DreamShaper XLImage | 256×256 | ~59.4s | D |
| Juggernaut XL v9Image | 256×256 | ~59.4s | D |
| Animagine XL 3.1Image | 256×256 | ~59.4s | D |
| Pony Diffusion V6 XLImage | 256×256 | ~59.4s | D |
| Animagine XL 4.0Image | 256×256 | ~59.4s | D |
| Illustrious XLImage | 256×256 | ~59.4s | D |
| Wan Video 2.1 1.3BVideo | 256×256 | ~38.6s/frame | F |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~1m 32s | F |
| Flux.2 Klein 4BImage | 256×256 | ~15.8s | F |
| LTX Video 2BVideo | 256×256 | ~45.9s/frame | F |
| KolorsImage | 256×256 | ~1m 46s | F |
| Stable CascadeImage | 256×256 | ~2m 12s | F |
| AuraFlow v0.3Image | 256×256 | ~3m 58s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~4m 51s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~52.8s | F |
| CogVideoX 2BVideo | 256×256 | ~45.9s/frame | F |
| HunyuanVideoVideo | 256×256 | ~1m 37s/frame | F |
| ChromaImage | 256×256 | ~52.8s | F |
| Z-Image TurboImage | 256×256 | ~54.5s | F |
| Flux.1 DevImage | 256×256 | ~3m 58s | F |
| Flux.1 SchnellImage | 256×256 | ~46.2s | F |
| LTX Video 13BVideo | 256×256 | ~1m 37s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~4m 24s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~24.1s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~1m 16s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~1m 6s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~1m 6s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~26.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~3m 45s | F |
| Mochi 1 PreviewVideo | 256×256 | ~1m 27s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~1m 21s/frame | F |
| Helios 14BVideo | 256×256 | ~1m 40s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~1m 40s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~1m 40s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~1m 40s/frame | F |
| Qwen ImageImage | 256×256 | ~1m 29s | F |
| Qwen Image EditImage | 256×256 | ~1m 29s | F |
| Flux.2 DevImage | 256×256 | ~41m 39s | F |
| MAGI-1Video | 256×256 | ~2m 4s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~2m 37s | 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 3050 8GB次のステップUnlocks 38 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 18%.
〜$249 MSRP
RTX 3070 8GBNVIDIAアップグレードUnlocks 38 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 103%.
〜$499 MSRP
Unlocks 112 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 34%.
〜$329 MSRP
Unlocks 193 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 595%.
〜$8,000 MSRP
GTX 1060 6GB (6 GB VRAM) can run these top models: Qwen 3.5 4B (score: 91/100), Phi-4 Mini Reasoning 4B (score: 89/100), Jina Embeddings v3 (score: 86/100). See the full compatibility list above.
GTX 1060 6GB has 6 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, GTX 1060 6GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on GTX 1060 6GB, we recommend Gemma 4 E2B. It achieves 30.0 tokens per second with 42K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
There are 4 upgrade path(s) from GTX 1060 6GB: RTX 3050 8GB, RTX 3070 8GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 6 GB, GTX 1060 6GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
GTX 1060 6GB (6 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.
GTX 1060 6GB has limited capability for AI image generation with only 6 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 6 GB, GTX 1060 6GB can run the 4B variant at Q4 (2.4 GB). Consider upgrading memory capacity for larger Qwen models.
With 6 GB on GTX 1060 6GB, 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 GTX 1060 6GB, 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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