The RTX 4070 SUPER improves on the base 4070 with more CUDA cores. At 56 tokens per second, it delivers faster inference while maintaining the 12GB VRAM capacity for efficient 7B-9B models.
| GPU | VRAM | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 4070 | 12 GB | 52 tok/s | 504 GB/s | $579 |
| RTX 5070 | 12 GB | 59 tok/s | 672 GB/s | $579 |
| RTX 4070 SUPER | 12 GB | 56 tok/s | 504 GB/s | $759 |
| RTX 4070 Ti SUPER | 16 GB | 72 tok/s | 672 GB/s | $1,148 |
Qwen / 4B / Q4_K_M / ~3.5 GB
Best for: Coding, Agents, Multimodal·Pop: 88/100
Perf: ~100.9 tok/s · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for coding, agents, multimodal on RTX 4070 SUPER.
ollama run qwen3.5:4b-instruct-q4_K_M
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 94/100
Perf: ~56.0 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
ollama run llama3.1:8b-instruct-q4_K_M
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~50.7 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4070 SUPER.
ollama run qwen3.5:9b-instruct-q4_K_M
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~56.0 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
ollama run qwen3:8b-q4_K_M
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 90/100
Perf: ~62.7 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
ollama run mistral:7b-instruct-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 85/100
Perf: ~62.7 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for coding on RTX 4070 SUPER.
ollama run qwen2.5-coder:7b-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 86/100
Perf: ~62.7 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
ollama run qwen2.5:7b-instruct-q4_K_M
LFM2 / 8B / Q4_K_M / ~6 GB
Best for: Local agents, tool calling, fast chat·Pop: 75/100
Perf: ~56.0 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for local agents, tool calling, fast chat on RTX 4070 SUPER.
ollama run liquidai/lfm2:8b-a1b-instruct-q4_K_M
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 77/100
Perf: ~62.7 tok/s · first token ~0.4s
Fits in 12 GB VRAM with room to spare. Best for reasoning, coding on RTX 4070 SUPER.
ollama run deepseek-r1-distill:qwen-7b-q4_K_M
Gemma / 4B / Q4_K_M / ~3.5 GB
Best for: Chat, Coding·Pop: 81/100
Perf: ~100.9 tok/s · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
ollama run gemma3:4b-instruct-q4_K_M
With 12GB VRAM, the RTX 4070 SUPER can run up to 9b parameter models. Top recommendations include Qwen3.5 4B Instruct, Llama 3.1 8B Instruct, Qwen3.5 9B Instruct.
The RTX 4070 SUPER achieves 56 tokens per second with Qwen3 8B at Q4 quantization. Smaller models run faster, larger models slower.
12GB VRAM is sufficient for local AI. You can comfortably run up to 9b parameter models with room for KV cache. 10 of our top 10 recommended models run at full speed.
Install Ollama from ollama.com, then run models directly. For example: ollama run qwen3.5:4b-instruct-q4_K_M. Ollama automatically detects your NVIDIA GPU and uses CUDA acceleration.
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.
Open ModelFit Wizard →