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 NVIDIA T4 is a compact Turing-generation inference GPU built for data centers and cloud providers, featuring 16 GB of GDDR6 in a 70W single-slot passive form factor. It was the first NVIDIA accelerator to make INT8 inference a first-class use case, and it became the most widely deployed GPU for inference workloads in hyperscale cloud environments. While modest by current standards, it remains available on AWS (G4dn), GCP, and Azure at low cost per hour. It can handle 7B models with Q4 quantization but will struggle with anything larger.
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 natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Turing is NVIDIA's first-generation RTX architecture, introducing dedicated RT and Tensor Cores to consumer GPUs for the first time. Built on TSMC's 12nm FinFET process.
AI Relevance
The first consumer architecture with Tensor Cores, enabling meaningful acceleration for INT8 and FP16 inference. However, limited VRAM (typically 6-11 GB) restricts modern LLM model sizes.
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11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。
16.0 GB
VRAM
$2,200
希望小売価格
$138/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.
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
Unlocks 2 additional models that do not fit on the current setup.
もっと余裕が欲しいですか？ MacBook Pro M3 24GB (24.0 GB unified memory) 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
SThis 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, lm-studio.
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 9BQwen 3 8BQwen 3 14BQwen 3.5 4B
Phi-4-reasoning-plus 14BNemotron Nano 8BPhi-4 Mini Reasoning 4B
Ministral 3 14B
Jina Embeddings v3
GPT-OSS 20B
BGE M3Qwen3-Coder 30B A3B InstructQwen 3.5 397B A17BDevstral 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 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72BGPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671B
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
31 of 52 models can generate images or video on your NVIDIA T4 16GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 700ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.5s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.5s | S |
| DreamShaper 8Image | 512×768 | ~1.5s | S |
| LCM DreamShaper v7Image | 512×768 | 400ms | S |
| PixArt-SigmaImage | 1024×1024 | ~5.9s | S |
| FramePack I2VVideo | 256×256 | ~10.8s/frame | S |
| SDXL TurboImage | 512×512 | 700ms | S |
| SDXL LightningImage | 1024×1024 | ~2.2s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~5.9s | S |
| Playground v2.5Image | 1024×1024 | ~8.9s | S |
| RealVisXL v5.0Image | 1024×1024 | ~6.6s | S |
| DreamShaper XLImage | 1024×1024 | ~6.6s | S |
| Juggernaut XL v9Image | 1024×1024 | ~6.6s | S |
| Animagine XL 3.1Image | 1024×1024 | ~6.6s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~6.6s | S |
| Animagine XL 4.0Image | 1024×1024 | ~6.6s | S |
| Illustrious XLImage | 1024×1024 | ~6.6s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~4.3s/frame | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~31s | S |
| Flux.2 Klein 4BImage | 256×256 | ~4s | S |
| LTX Video 2BVideo | 256×256 | ~5.1s/frame | S |
| KolorsImage | 256×256 | ~31.3s | A |
| Stable CascadeImage | 1024×1024 | ~14.8s | B |
| AuraFlow v0.3Image | 256×256 | ~52.4s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 28s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~15.9s | B |
| CogVideoX 2BVideo | 256×256 | ~5.1s/frame | D |
| HunyuanVideoVideo | 256×256 | ~10.8s/frame | D |
| ChromaImage | 256×256 | ~5.9s | D |
| Z-Image TurboImage | 256×256 | ~12.2s | D |
| Flux.1 DevImage | 256×256 | ~26.6s | F |
| Flux.1 SchnellImage | 256×256 | ~5.2s | F |
| LTX Video 13BVideo | 256×256 | ~10.8s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~29.5s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~2.7s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~8.5s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~7.4s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~7.4s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~3s | F |
| Flux.1 Fill DevImage | 256×256 | ~25.1s | F |
| Mochi 1 PreviewVideo | 256×256 | ~9.8s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~9.1s/frame | F |
| Helios 14BVideo | 256×256 | ~11.2s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~11.2s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~11.2s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~11.2s/frame | F |
| Qwen ImageImage | 256×256 | ~9.9s | F |
| Qwen Image EditImage | 256×256 | ~9.9s | F |
| Flux.2 DevImage | 256×256 | ~4m 39s | F |
| MAGI-1Video | 256×256 | ~13.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~17.5s | 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
Unlocks 2 additional models that do not fit on the current setup.
〜$1,099 MSRP
RTX A4500 20GBNVIDIAアップグレードUnlocks 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 88%.
〜$2,000 MSRP
Intel Arc Pro B60 24GBコスパ最良Unlocks 36 additional models that do not fit on the current setup.
〜$599 MSRP
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 341%.
〜$8,000 MSRP
NVIDIA T4 16GB (16 GB VRAM) can run these top models: Qwen 3.5 9B (score: 95/100), Qwen 3 8B (score: 92/100), Qwen 3 14B (score: 91/100). See the full compatibility list above.
NVIDIA T4 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, NVIDIA T4 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on NVIDIA T4 16GB, we recommend Qwen 3.5 9B. It achieves 40.7 tokens per second with 58K 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 NVIDIA T4 16GB: MacBook Pro M3 24GB, RTX A4500 20GB. Upgrading would unlock larger models and faster inference speeds.
NVIDIA T4 16GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.
NVIDIA T4 16GB (16 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
NVIDIA T4 16GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 16 GB of usable memory is sufficient for most image generation workflows at standard resolutions.
Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for NVIDIA T4 16GB with 16 GB. You can run the 9B variant at Q8 (9.6 GB) for excellent quality, or try the 35B-A3B MoE variant at Q4 if it fits your context needs.
With 16 GB on NVIDIA T4 16GB, use Q8_0 for 8B models (best quality), Q4_K_M for 14B models (good balance), and Q4_K_M with limited context for larger models. Avoid going below Q4 — quality drops sharply at Q2-Q3.
NVIDIA T4 16GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.
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