 |
VOOZH | about |
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 1070 8GB is a Pascal card with compute capability 6.1 and no Tensor Cores. Its 8 GB VRAM is enough for 7B models at Q4 via llama.cpp or Ollama, but all inference runs on CUDA cores without any accelerated INT8 path. Token generation is slow compared to any RTX-era GPU. Like all Pascal cards, it faces imminent CUDA deprecation with toolkit version 13.x. Useful as a free GPU you already own, but not worth purchasing for AI purposes.
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
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.
Chat
SQwen 3.5 4B 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
ACodestral Mamba 7B is a specialized fit for Coding. 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.
Agentic Coding
AJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
21 of 52 models can generate images or video on your GTX 1070 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~4.4s | S |
| Stable Diffusion 1.5Image | 512×768 | ~8.8s | S |
| Realistic Vision v5.1Image | 512×768 | ~8.8s | S |
| DreamShaper 8Image | 512×768 | ~8.8s | S |
| LCM DreamShaper v7 |
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 98%.
~$699 MSRP
Unlocks 34 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 98%.
~$999 MSRP
Unlocks 74 additional models that do not fit on the current setup.
~$329 MSRP
Unlocks 155 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 393%.
~$8,000 MSRP
Compare with similar
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~1m 33s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~2m 38s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~3m 14s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~30.6s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 30s/frame |
Gemma 4 E2B 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
ACodestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. 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.
RAG
AGranite 4.1 3B 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 |
| ~2.6s |
| S |
| PixArt-SigmaImage | 256×256 | ~35.2s | S |
| FramePack I2VVideo | 256×256 | ~1m 5s/frame | A |
| SDXL TurboImage | 256×256 | ~11.7s | A |
| SDXL LightningImage | 256×256 | ~35s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~1m 33s | B |
| Playground v2.5Image | 256×256 | ~52.8s | B |
| RealVisXL v5.0Image | 256×256 | ~1m 45s | B |
| DreamShaper XLImage | 256×256 | ~1m 45s | B |
| Juggernaut XL v9Image | 256×256 | ~1m 45s | B |
| Animagine XL 3.1Image | 256×256 | ~1m 45s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~1m 45s | B |
| Animagine XL 4.0Image | 256×256 | ~1m 45s | B |
| Illustrious XLImage | 256×256 | ~1m 45s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~25.7s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~1m 2s | D |
| Flux.2 Klein 4BImage | 256×256 | ~10.6s | D |
| LTX Video 2BVideo | 256×256 | ~30.6s/frame | F |
| KolorsImage | 256×256 | ~1m 10s | F |
| Stable CascadeImage | 256×256 | ~1m 28s | F |
| AuraFlow v0.3Image | 256×256 | ~2m 38s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~3m 14s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~35.2s | F |
| CogVideoX 2BVideo | 256×256 | ~30.6s/frame | F |
| HunyuanVideoVideo | 256×256 | ~1m 5s/frame | F |
| ChromaImage | 256×256 | ~35.2s | F |
| Z-Image TurboImage | 256×256 | ~36.3s | F |
| Flux.1 DevImage | 256×256 | ~2m 38s | F |
| Flux.1 SchnellImage | 256×256 | ~30.8s | F |
| LTX Video 13BVideo | 256×256 | ~1m 5s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~2m 56s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~16.1s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~50.5s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~44.1s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~44.1s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~17.6s | F |
| Flux.1 Fill DevImage | 256×256 | ~2m 30s | F |
| Mochi 1 PreviewVideo | 256×256 | ~58.2s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~54s/frame | F |
| Helios 14BVideo | 256×256 | ~1m 7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~1m 7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~1m 7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~1m 7s/frame | F |
| Qwen ImageImage | 256×256 | ~59.3s | F |
| Qwen Image EditImage | 256×256 | ~59.3s | F |
| Flux.2 DevImage | 256×256 | ~27m 46s | F |
| MAGI-1Video | 256×256 | ~1m 23s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 44s | 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.
There are 4 upgrade path(s) from GTX 1070 8GB: RTX 3080 10GB, RTX 2080 Ti 11GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
8.0 GB
VRAM
$379
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.
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 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.
