Best local AI models for NVIDIA GTX 1070 Laptop
8 GB GDDR5. At a 4k context, 123 of the 233 models in our catalog with verified parameter counts fit fully, up to Mochi 1 at 10B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 123 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Mochi 1 | 10B | Q4_K_M | 7.3 GB |
| Gemma 2 9B | 9B | Q4_K_M | 8 GB |
| Nemotron Nano 4B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4 9B / GLM-4.5-Air | 9B | Q5_K_M | 7.7 GB |
| Yi-Coder 1.5B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4-9B-Chat / CodeGeeX4 | 9B | Q5_K_M | 7.7 GB |
| GLM-4V-9B / GLM-4.1V-Thinking | 9B | Q5_K_M | 7.7 GB |
| Chroma | 8.9B | Q5_K_M | 7.6 GB |
| Llama 3.1 8B | 8B | Q5_K_M | 7.4 GB |
| Granite 3.3 2B / 8B | 8B | Q6_K | 7.9 GB |
| Ministral 3B / 8B | 8B | Q6_K | 7.9 GB |
| InternLM 3 8B | 8B | Q6_K | 7.9 GB |
| OpenCoder 1.5B / 8B | 8B | Q6_K | 7.9 GB |
| Seed-Coder 8B | 8B | Q6_K | 7.9 GB |
| MiniCPM-V 2.6 / MiniCPM-o 2.6 | 8B | Q6_K | 7.9 GB |
| Idefics 3 8B | 8B | Q6_K | 7.9 GB |
| Fuyu-8B | 8B | Q6_K | 7.9 GB |
| Emu3 | 8B | Q6_K | 7.9 GB |
| Stable Diffusion 3.5 Large / Turbo | 8B | Q6_K | 7.9 GB |
| EXAONE 3.5 2.4B / 7.8B | 7.8B | Q6_K | 7.7 GB |
| Mistral 7B | 7B | Q6_K | 7.4 GB |
| Qwen2.5 0.5B / 1.5B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| OLMo 2 1B / 7B | 7B | Q6_K | 6.9 GB |
| Falcon 3 1B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| Command R7B | 7B | Q6_K | 6.9 GB |
| OpenHermes 2.5 | 7B | Q6_K | 6.9 GB |
| Zephyr 7B Beta | 7B | Q6_K | 6.9 GB |
| OpenChat 3.5 | 7B | Q6_K | 6.9 GB |
| Starling LM 7B | 7B | Q6_K | 6.9 GB |
| Codestral Mamba 7B | 7B | Q6_K | 6.9 GB |
Close, but only with CPU offload
These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.
| Model | Parameters | Memory at Q4_K_M | System RAM at 4k |
|---|---|---|---|
| Open-Sora 2.0 | 11B | 8.1 GB needed | 10.1 GB |
| FLUX.1 dev | 12B | 14.4 GB needed | 16.4 GB |
| Gemma 3 12B | 12B | 8.8 GB needed | 10.8 GB |
| Gemma 4 12B | 12B | 8.8 GB needed | 10.8 GB |
| Mistral NeMo 12B | 12B | 8.8 GB needed | 10.8 GB |
| Pixtral 12B | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 schnell | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Kontext dev | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Krea dev | 12B | 8.8 GB needed | 10.8 GB |
| Vicuna 13B | 13B | 9.5 GB needed | 11.5 GB |
How to read this
The NVIDIA GTX 1070 Laptop graphics card features 8 GB GDDR5 memory. This dedicated video memory determines the maximum size of the AI model you can run entirely on your hardware. For the best performance, the model files must fit within this 8 GB limit. If a model exceeds this capacity, your system must use slower system memory to process the remaining data.
The quantization column shows the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. A Q4_K_M quant offers a balance of speed and quality. A Q5_K_M or Q6_K quant provides higher accuracy but requires more memory. For example, Mochi 1 10B fits at Q4_K_M using 7.3 GB of memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB of memory.
Smaller models can run at higher quantization levels on this hardware. Llama 3.1 8B fits at Q5_K_M using 7.4 GB of memory. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large fit at Q6_K using 7.9 GB of memory. EXAONE 3.5 7.8B fits at Q6_K using 7.7 GB of memory. Mistral 7B fits at Q6_K using 7.4 GB of memory.
Other models fit comfortably within the limit at high precision. Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all fit at Q6_K using 6.9 GB of memory. Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V 9B fit at Q5_K_M using 7.7 GB of memory. Chroma 8.9B fits at Q5_K_M using 7.6 GB of memory.
You can run larger models using CPU offload if your laptop has 32 GB of system RAM. Offloading splits the workload between your graphics card and your system processor. This process allows you to run Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev at Q4_K_M. These models need 8.8 GB of video memory and 10.8 GB of system RAM. Vicuna 13B needs 9.5 GB of video memory and 11.5 GB of system RAM.
Other offload options require different amounts of memory. Open-Sora 2.0 11B needs 8.1 GB of video memory and 10.1 GB of system RAM at Q4_K_M. FLUX.1 dev 12B needs 14.4 GB of video memory and 16.4 GB of system RAM at FP8. Offloading makes these larger models run much slower because system RAM is slower than GDDR5 video memory.
Memory calculations assume a standard 4k context window. The context window is the amount of text the model can remember during a conversation. Running longer conversations or processing larger documents increases memory usage. If you exceed the 4k context limit, the model will require more memory and may overflow your 8 GB video memory.