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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 5070 Ti 16GB is the best mid-range AI card in the Blackwell lineup, combining 16 GB of GDDR7 VRAM with 896 GB/s bandwidth and 5th-gen Tensor Cores including FP4 support. This bandwidth is substantially better than the Ada-era RTX 4070 Ti Super 16GB (672 GB/s), meaning decode on 13B–30B Q4 models is noticeably faster. At $749, it competes directly with the Ada RTX 4080 Super 16GB at a lower price and with better architecture. The best 16 GB AI card currently available under $1000.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
Architecture
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
Cost vs cloud API
Assumes 4 hours/day of active inference at 112 tok/s, RTX 5070 Ti 16GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
48.5M
Tokens/month at this pace
$25.9
Monthly local cost
$485
Same tokens on cloud API
$0.535
Local $/1M tokens
Break-even: pays for itself in 1.6 months vs cloud API at this workload. Price reference: $749 MSRP.
Chat
SQwen 3.5 9B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Coding
SQwen 3.5 9B is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Agentic Coding
SJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
31 of 52 models can generate images or video on your RTX 5070 Ti 16GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1.1s | S |
| Stable Diffusion 1.5Image | 512×768 | ~2.2s | S |
| Realistic Vision v5.1Image | 512×768 | ~2.2s | S |
| DreamShaper 8Image | 512×768 | ~2.2s | S |
| LCM DreamShaper v7 |
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 2 additional models that do not fit on the current setup.
~$1,099 MSRP
Unlocks 14 additional models that do not fit on the current setup.
~$2,000 MSRP
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 90%.
~$8,000 MSRP
Compare with similar
Related guides
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~8.9s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~39.8s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~2m 12s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~7.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~22.6s/frame |
Qwen 3.5 9B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Reasoning
SQwen 3.5 9B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
RAG
AGranite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
| 512×768 |
| 700ms |
| S |
| PixArt-SigmaImage | 1024×1024 | ~8.9s | S |
| FramePack I2VVideo | 256×256 | ~16.3s/frame | S |
| SDXL TurboImage | 512×512 | ~1.1s | S |
| SDXL LightningImage | 1024×1024 | ~3.3s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~8.9s | S |
| Playground v2.5Image | 1024×1024 | ~13.3s | S |
| RealVisXL v5.0Image | 1024×1024 | ~10s | S |
| DreamShaper XLImage | 1024×1024 | ~10s | S |
| Juggernaut XL v9Image | 1024×1024 | ~10s | S |
| Animagine XL 3.1Image | 1024×1024 | ~10s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~10s | S |
| Animagine XL 4.0Image | 1024×1024 | ~10s | S |
| Illustrious XLImage | 1024×1024 | ~10s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~6.5s/frame | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~46.5s | S |
| Flux.2 Klein 4BImage | 256×256 | ~6s | S |
| LTX Video 2BVideo | 256×256 | ~7.7s/frame | S |
| KolorsImage | 256×256 | ~47s | A |
| Stable CascadeImage | 1024×1024 | ~22.1s | B |
| AuraFlow v0.3Image | 256×256 | ~1m 19s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~2m 12s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~23.9s | B |
| CogVideoX 2BVideo | 256×256 | ~7.7s/frame | D |
| HunyuanVideoVideo | 256×256 | ~16.3s/frame | D |
| ChromaImage | 256×256 | ~8.9s | D |
| Z-Image TurboImage | 256×256 | ~18.3s | D |
| Flux.1 DevImage | 256×256 | ~39.8s | F |
| Flux.1 SchnellImage | 256×256 | ~7.7s | F |
| LTX Video 13BVideo | 256×256 | ~16.3s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~44.3s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~4s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~12.7s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~11.1s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~11.1s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~4.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~37.6s | F |
| Mochi 1 PreviewVideo | 256×256 | ~14.6s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~13.6s/frame | F |
| Helios 14BVideo | 256×256 | ~16.7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~16.7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~16.7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~16.7s/frame | F |
| Qwen ImageImage | 256×256 | ~14.9s | F |
| Qwen Image EditImage | 256×256 | ~14.9s | F |
| Flux.2 DevImage | 256×256 | ~6m 59s | F |
| MAGI-1Video | 256×256 | ~20.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~26.2s | 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.
Buying advice
Usable for local AI with limits
Can run 11 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
16.0 GB
VRAM
$749
MSRP
$47/GB
Cost per GB VRAM
Best models for this GPU
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 2 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.
