The RTX 4070 has 12 GB of GDDR6X and 504 GB/s of bandwidth, the same memory speed as the RTX 4070 Super. It is a capable 8B to 12B card.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 4070 specs that matter for local AI
- Memory: 12 GB GDDR6X on a 192-bit bus
- Memory bandwidth: 504 GB/s
- Board power: 200 W
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?
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
- Llama 3.1 8B and Qwen3 8B: about 7 GB at Q4 with 8K context.
- Gemma 3 12B (Q4_K_M): about 9.3 GB.
- Qwen3 14B (Q4_K_M): about 11.4 GB, 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
gpt-oss-20b (about 14 GB) and 24B+ models do not fit in 12 GB at Q4.
A solid 12 GB card. It runs 8B to 12B models comfortably. Because it shares bandwidth with the 4070 Super, AI speed is about the same on both.
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. 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 is at the top of this range.
Frequently asked questions
Can the RTX 4070 run Llama 3.1 8B?
Yes, easily: about 7 GB at Q4_K_M with an 8K context.
Is 12 GB enough for AI?
For 8B to 12B models, yes. For 20B and larger, no.
RTX 4070 vs 4070 Super for AI?
Both have 12 GB and 504 GB/s, so speed is similar.
Sources and method
- RTX 4070 specs: GamersNexus’ 40-series specs and Wikipedia’s GeForce 40 series table.
- Measured speeds: 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.
