Best AI Models for NVIDIA GeForce RTX 4070 (12.0GB)
12 GB is the sweet spot for entry into local AI. It runs 7B–13B models at good quality quantizations, making it a practical and affordable starting point for running LLMs on your own hardware.
This memory tier, common on GPUs like the RTX 3060 12GB, is surprisingly capable for local AI. You can run Llama 3 8B, Mistral 7B, and similar 7B models at Q4_K_M quantization with decent token generation speed. Smaller models like Phi 3 Mini (3.8B) run at Q6 or Q8 with room to spare. Reaching up to 13B models is possible at Q2–Q3 quantization, though quality trade-offs become more noticeable.
Runs Well
- 7B models at Q4_K_M quality
- Small models (3B–4B) at Q5–Q8
- Chat and coding assistants for everyday use
Challenging
- 13B models only at Q2–Q3 (lower quality)
- 14B+ models do not fit
- Context windows limited for 7B+ models
What LLMs Can NVIDIA GeForce RTX 4070 Run?
Showing compatibility for NVIDIA GeForce RTX 4070
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 9.1 GB76% | 35.9 t/s | 16K | GREAT FIT | S89 | |
| Q4_K_M | 7.9 GB66% | 41.4 t/s | 33K | GOOD FIT | A83 | |
| Q4_K_M | 5.5 GB46% | 59.3 t/s | 41K | FAIR FIT | B61 | |
| Q4_K_M | 6.1 GB51% | 53.7 t/s | 8K | GOOD FIT | A66 | |
| Q4_K_M | 5.3 GB44% | 62.0 t/s | 131K | FAIR FIT | B59 | |
| Q4_K_M | 5.0 GB42% | 65.7 t/s | 33K | FAIR FIT | B57 | |
| Q4_K_M | 5.4 GB45% | 61.0 t/s | 131K | FAIR FIT | B60 | |
| Q4_K_M | 5.4 GB45% | 60.8 t/s | 131K | FAIR FIT | B60 |
NVIDIA GeForce RTX 4070 Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM
- 12.0 GB GDDR6X
- Memory Bandwidth
- 504.0 GB/s
- CUDA Cores
- 5,888
- Tensor Cores
- 184
- FP16 Performance
- 58.30 TFLOPS
- TDP
- 200W
- Release Date
- 2023-04-13
- MSRP
- $599
Get Started
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NVIDIA GeForce RTX 3060 12GB
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NVIDIA GeForce RTX 3080 Ti
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NVIDIA GeForce RTX 4070 SUPER
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Frequently Asked Questions
- Can NVIDIA GeForce RTX 4070 run Llama 3 8B?
Yes, the NVIDIA GeForce RTX 4070 with 12 GB can run Llama 3 8B at Q4_K_M quantization with good performance. At this VRAM level, you can expect smooth token generation and responsive inference for chat and coding tasks.
- Is NVIDIA GeForce RTX 4070 good for AI?
The NVIDIA GeForce RTX 4070 has 12 GB of GDDR6X, making it solid for running local LLM models. 7B models run well at Q4 quality, and smaller models shine.
- How many parameters can NVIDIA GeForce RTX 4070 handle?
With 12 GB, the NVIDIA GeForce RTX 4070 can handle models up to approximately 7-14B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 20B parameters.
- What quantization should I use on NVIDIA GeForce RTX 4070?
For the best balance of quality and speed on 12 GB, Q4_K_M is the recommended starting point. If you have headroom, try Q5_K_M for better quality. For larger models that barely fit, Q3_K_M or Q2_K can squeeze them in at the cost of some output quality.
- How fast is NVIDIA GeForce RTX 4070 for AI inference?
Speed depends on the model size and quantization. With 504.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 4070 can typically achieve 15-35 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.