What can an RTX 3070 run?
The RTX 3070 has 8GB of VRAM and 448 GB/s of memory bandwidth. Run through the same engine as our calculator, it runs 16 of 51 current local LLMs fully on the GPU — the biggest is Qwen3.5 9B.
Every model, checked against the RTX 3070
Best quant means the highest-quality quantization that still fits fully in 8GB at 8K context. Speeds are llama.cpp estimates from memory bandwidth — the same math the calculator uses.
| Model | Best quant | VRAM needed | Est. speed | Status |
|---|---|---|---|---|
| Llama 3.2 1B Llama | Q8_0 | 2.3 GB | ~200 tok/s | Runs on GPU |
| Qwen3 0.6B Qwen | Q8_0 | 1.7 GB | ~200 tok/s | Runs on GPU |
| Gemma 3 1B Gemma | Q8_0 | 2.0 GB | ~200 tok/s | Runs on GPU |
| Llama 3.2 3B Llama | Q8_0 | 5.1 GB | ~88 tok/s | Runs on GPU |
| Qwen3 1.7B Qwen | Q8_0 | 3.4 GB | ~139 tok/s | Runs on GPU |
| Phi-4-mini Phi | Q8_0 | 5.9 GB | ~75 tok/s | Runs on GPU |
| Qwen3 4B Qwen | Q8_0 | 5.8 GB | ~76 tok/s | Runs on GPU |
| Gemma 3 4B Gemma | Q8_0 | 6.8 GB | ~64 tok/s | Runs on GPU |
| Llama 3.1 8B Llama | Q5_K_M | 7.6 GB | ~56 tok/s | Runs on GPU |
| Qwen3 8B Qwen | Q5_K_M | 7.9 GB | ~54 tok/s | Runs on GPU |
| Mistral 7B v0.3 Mistral | Q6_K | 7.9 GB | ~54 tok/s | Runs on GPU |
| Ministral 8B Mistral | Q5_K_M | 7.8 GB | ~55 tok/s | Runs on GPU |
| gpt-oss 20B OpenAI | — | 14.2 GB | — | Won't fit |
| Qwen3 14B Qwen | — | 11.3 GB | — | Won't fit |
| Phi-4 14B Phi | — | 11.6 GB | — | Won't fit |
| Gemma 3 12B Gemma | — | 11.4 GB | — | Won't fit |
| Mistral Small 3.1 24B Mistral | — | 17.5 GB | — | Won't fit |
| Gemma 3 27B Gemma | — | 23.4 GB | — | Won't fit |
| Gemma 4 31B Gemma | — | 25.3 GB | — | Won't fit |
| Gemma 4 26B-A4B Gemma | — | 18.0 GB | — | Won't fit |
| Qwen3 32B Qwen | — | 22.7 GB | — | Won't fit |
| Qwen3 30B-A3B Qwen | — | 20.3 GB | — | Won't fit |
| Qwen3-Coder 30B-A3B Qwen | — | 20.3 GB | — | Won't fit |
| DeepSeek-R1 Distill 32B DeepSeek | — | 23.5 GB | — | Won't fit |
| Llama 3.3 70B Llama | — | 48.0 GB | — | Won't fit |
| Llama 4 Scout Llama | — | 71.3 GB | — | Won't fit |
| gpt-oss 120B OpenAI | — | 75.2 GB | — | Won't fit |
| Qwen3 235B-A22B Qwen | — | 150.4 GB | — | Won't fit |
| Llama 4 Maverick Llama | — | 255.8 GB | — | Won't fit |
| DeepSeek-V3 DeepSeek | — | 427.8 GB | — | Won't fit |
| DeepSeek-R1 DeepSeek | — | 427.8 GB | — | Won't fit |
| Qwen3.5 9B Qwen | Q5_K_M | 7.6 GB | ~57 tok/s | Runs on GPU |
| Qwen3.5 35B-A3B Qwen | — | 22.9 GB | — | Won't fit |
| Qwen3.6 35B-A3B Qwen | — | 22.9 GB | — | Won't fit |
| Qwen3.6 27B Qwen | — | 18.2 GB | — | Won't fit |
| Qwen3.8 27B Qwen | — | 18.7 GB | — | Won't fit |
| Gemma 4 E4B Gemma | Q5_K_M | 7.3 GB | ~98 tok/s | Runs on GPU |
| DeepSeek V4-Flash DeepSeek | — | 181.4 GB | — | Won't fit |
| GLM-5.3-Flash GLM | — | 203.6 GB | — | Won't fit |
| Kimi K2.6 Kimi | — | 635.6 GB | — | Won't fit |
| MiniMax M3 MiniMax | — | 272.9 GB | — | Won't fit |
| Mistral Small 4 Mistral | — | 76.3 GB | — | Won't fit |
| Kimi K3 Kimi | — | 1763.3 GB | — | Won't fit |
| Muse Spark Open Muse | — | 520.0 GB | — | Won't fit |
| Flint Recursive | — | 26.1 GB | — | Won't fit |
| Beam 1.0 Reflection | — | 318.7 GB | — | Won't fit |
| MiniMax M3.1 Flash MiniMax | — | 272.9 GB | — | Won't fit |
| Mistral Large 4 Mistral | — | 667.3 GB | — | Won't fit |
| Ling-3.1-flash-1019 Ling | — | 355.8 GB | — | Won't fit |
| LFM3-1.7B Liquid | Q8_0 | 2.6 GB | ~196 tok/s | Runs on GPU |
| LFM3-VL-2B Liquid | Q8_0 | 2.8 GB | ~178 tok/s | Runs on GPU |
Want to run 70B models?
RTX 3070 FAQ
What's the biggest LLM an RTX 3070 can run?
Fully on-GPU at 8K context, the biggest model that fits the RTX 3070 (8GB) is Qwen3.5 9B at Q5_K_M (~57 tok/s). Larger models need a card with more VRAM or system-RAM offload, which is much slower.
How fast is an RTX 3070 for local AI?
On a mid-size model like Llama 3.1 8B (Q5_K_M), expect roughly 56 tokens/sec. Speed scales with memory bandwidth (448 GB/s) more than anything else.
Can an RTX 3070 run a 70B model?
Not fully on-GPU: a 70B at Q4 needs about 48 GB and the RTX 3070 has 8GB. It can crawl with system-RAM offload, but a bigger card or a rented cloud GPU is the practical route.
Is the RTX 3070 good for local AI in 2026?
It runs 16 of the 51 models we track — a workable entry point for small and mid-size models. The full breakdown is in the table above.
Keep exploring
More GPU guides: RTX 5090 · RTX 5080 · RTX 5070 · RTX 5060 Ti 16GB · All GPU guides
Related reading: How to Run an LLM Locally (Beginner's Guide) · Q4 vs Q8 Quantization: VRAM Explained · Best Budget GPUs for Local AI
