The RTX 4070 Super pairs 12 GB of GDDR6X with 504 GB/s of memory bandwidth, which makes it about 40 percent faster than an RTX 3060 12GB for token generation while offering the same amount of memory. It handles 8B to 12B models quickly, but 12 GB is still the limit that decides what you can run.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 4070 Super specs that matter for local AI
- Memory: 12 GB GDDR6X on a 192-bit bus
- Memory bandwidth: 504 GB/s
- Board power: 220 W
- Same bandwidth as the RTX 4070: both cards list 504 GB/s, so memory-bound AI speed is similar between them.
VRAM decides which models fit. Bandwidth decides how fast they generate text, because every new token means reading the whole model from memory.
Which AI models fit on the RTX 4070 Super?
The table assumes Q4_K_M quantization (about 4.9 bits per weight) and an 8K context. Totals include the KV cache and about 1 GB of runtime overhead. Speed is an estimate: memory bandwidth divided by model size, at 60 to 80 percent efficiency, which matches published llama.cpp results on several RTX cards.
| Model | Weights | KV cache (8K) | Total (est.) | On 12 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 123 to 164 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 61 to 82 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 60 to 80 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 41 to 54 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Tight | 33 to 45 tok/s |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | No | – |
| Mistral Small 3.2 24B | 14.7 GB | 1.3 GB | 17.0 GB | No | – |
| Qwen3.6 27B | 17.0 GB | 0.5 GB | 18.6 GB | No | – |
| Gemma 3 27B Instruct | 16.8 GB | 1.1 GB | 18.9 GB | No | – |
| Qwen3 32B | 20.1 GB | 2.1 GB | 23.2 GB | No | – |
| Qwen3.6 35B-A3B | 22.0 GB | 0.2 GB | 23.2 GB | No | – |
“Fits” means the estimate is at most 92 percent of VRAM. “Tight” means it is between 92 and 100 percent, so close other GPU apps. Mixture-of-Experts models (gpt-oss-20b and Qwen3.6 35B-A3B) need their full weights in memory, but they only use part of them per token, so speed is not estimated.
Best models for the RTX 4070 Super
- Llama 3.1 8B and Qwen3 8B: about 7 GB at Q4_K_M with 8K context. Q6_K or Q8_0 versions also fit if you want higher quality (Q8_0 is about 8.5 GB for the weights).
- Gemma 3 12B (Q4_K_M): about 9.3 GB, with a small KV cache thanks to sliding-window attention.
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K, a tight fit.
How context length changes things
The KV cache grows with every token of context, so a model that fits at 4K can fail at 32K.
| Model (Q4_K_M) | Context | KV cache | Total (est.) | On 12 GB |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | 4K | 0.54 GB | 6.5 GB | Fits |
| Llama 3.1 8B Instruct | 8K | 1.07 GB | 7.0 GB | Fits |
| Llama 3.1 8B Instruct | 16K | 2.15 GB | 8.1 GB | Fits |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | Fits |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | Fits |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | Tight |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | No |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | No |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost if you need more context.
What does not fit
The 20B to 32B class needs more than 12 GB at Q4: gpt-oss-20b about 14 GB, Mistral Small 3.2 24B about 17 GB, and the 27B to 32B models 19 to 23 GB. For those, look at 16 GB cards (for 20B-class) or 24 GB cards.
A fast 12 GB card for 8B to 12B models. If you mostly run 7B to 12B models, the 4070 Super is a strong, efficient choice at 220 W. If you want to run 20B and larger models, the extra VRAM of a 16 GB or 24 GB card will matter more than the speed difference.
Verdict
What speed to expect
At 504 GB/s, an 8B Q4 model has a ceiling near 102 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 61 to 82 tokens per second for Llama 3.1 8B, and 41 to 54 for Gemma 3 12B. We did not find a reliable measured result for this exact card, so these are estimates. For reference, an RTX 4070 Ti (also 504 GB/s) was measured at 83.5 tokens per second on Llama 3 8B Q4_K_M in a community llama.cpp dataset from May 2024, which sits at the top of that range.
Frequently asked questions
Can the RTX 4070 Super run a 13B or 14B model?
Yes at Q4_K_M with a short context. Qwen3 14B needs about 11.4 GB at 8K, which leaves very little spare.
Is the RTX 4070 Super better than the RTX 3060 12GB for AI?
It is faster (504 vs 360 GB/s), but both have 12 GB, so they can run the same models.
Can it run Llama 3.1 8B at Q8?
Yes. The Q8_0 weights are about 8.5 GB and the total is about 10.6 GB at 8K context, which fits in 12 GB.
Sources and method
- RTX 4070 Super specs: GamersNexus’ RTX 4080 Super, 4070 Ti Super and 4070 Super specs.
- RTX 4070 Ti reference speed: XiongjieDai’s GPU-Benchmarks-on-LLM-Inference on GitHub (llama.cpp, May 2024 snapshot).
- Linear-attention and sliding-window layers are handled with reduced KV cache; small fixed states in some newer models are not counted.
- Model architecture: each model’s published
config.jsonon Hugging Face (Llama 3.1 8B and Gemma 3 are read from the unsloth mirrors because the original repositories require a login). - VRAM and speed figures are estimates from our calculator, not our own benchmarks. Last updated 3 Nov 2026.
