NVIDIA RTX 4070
The NVIDIA RTX 4070 has 12 GB VRAM and 504 GB/s memory bandwidth. It can run 27 of our 80 tracked models natively in VRAM at 8k context.
With 12 GB GDDR6X, the NVIDIA RTX 4070 is a consumer-tier GPU that can run 27 models natively. It handles 13B-class models comfortably.
The NVIDIA RTX 4070 features 12GB GDDR6X at 504 GB/s with 5,888 CUDA cores. Same VRAM capacity as the 4070 Ti but with fewer compute units, keeping it firmly in the 7B model tier for comfortable local inference. A popular 1440p gaming card that doubles as an accessible entry point into running open-weight LLMs.
NVIDIA RTX 4070: 12GB GDDR6X at 504 GB/s — same VRAM as Ti but lower clocks.
Same model compatibility as 4070 Ti with ~10-15% lower tokens/sec.
Full CUDA support. Value 12GB option.
| Vendor | NVIDIA |
| Architecture | Ada Lovelace |
| VRAM | 12 GB |
| Memory type | GDDR6X |
| Memory bandwidth | 504 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2023 |
| Models (native) | 27 / 80 |
| Models (offload) | 22 / 80 |
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.
Popular models for this GPU
Models this GPU runs natively in VRAM (27)
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~37.2 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q2_K · ~59.8 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0NVFP4 · ~37.5 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7NVFP4 · ~36.6 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4NVFP4 · ~39.3 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6NVFP4 · ~44 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6NVFP4 · ~45.8 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2NVFP4 · ~35.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0NVFP4 · ~44.2 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3NVFP4 · ~64.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0NVFP4 · ~64.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7NVFP4 · ~62.9 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3NVFP4 · ~76.7 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0NVFP4 · ~69.7 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~38.5 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~36.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4NVFP4 · ~64 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~44.6 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~50.4 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~53.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~39 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~39 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~52.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~62.7 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~75.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~156 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~184.5 t/s
Models that fit with CPU offload (22)
These use system RAM for layers that don't fit in VRAM — expect much slower inference.
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~1.3 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q2_K · ~1.3 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q2_K · ~1.3 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q2_K · ~1.3 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7NVFP4 · ~1.9 t/s
- Command-R 35B35B · MMLU-Pro 33.0NVFP4 · ~1.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2NVFP4 · ~10.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2NVFP4 · ~2.7 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0NVFP4 · ~2.9 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5NVFP4 · ~3.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0NVFP4 · ~3.2 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4NVFP4 · ~3.2 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0NVFP4 · ~3.2 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3NVFP4 · ~12.8 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2NVFP4 · ~3.1 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5NVFP4 · ~12.9 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0NVFP4 · ~4.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5NVFP4 · ~5.5 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2NVFP4 · ~5.5 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6NVFP4 · ~12 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8NVFP4 · ~8.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2NVFP4 · ~10.2 t/s
Too large for this GPU (31)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
Compare NVIDIA RTX 4070 with other GPUs
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Frequently asked questions
- How much VRAM does the NVIDIA RTX 4070 have?
- The NVIDIA RTX 4070 has 12 GB of GDDR6X with 504 GB/s memory bandwidth.
- What is the NVIDIA RTX 4070 best for?
- With 12 GB of VRAM, the NVIDIA RTX 4070 is best for running compact models (1B–8B) at low quantization, suitable for edge inference, prototyping, and lightweight tasks.
- What LLMs can the NVIDIA RTX 4070 run locally?
- The NVIDIA RTX 4070 can run 27 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at NVFP4, Llama 3.2 3B Instruct at BF16, Llama 3.2 1B Instruct at FP32.
- Can the NVIDIA RTX 4070 run Llama 3.3 70B Instruct?
- The NVIDIA RTX 4070 can run Llama 3.3 70B Instruct with CPU offload at Q2_K quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 4070 run Qwen 3.6 27B?
- The NVIDIA RTX 4070 can run Qwen 3.6 27B with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 4070 run Llama 3.1 8B Instruct?
- Yes. The NVIDIA RTX 4070 runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 64.6 tokens per second.