The RTX 5070 pairs 12 GB of GDDR7 with 672 GB/s of bandwidth. It is fast for 8B to 12B models, but 12 GB is the ceiling 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 5070 specs that matter for local AI
- Memory: 12 GB GDDR7 on a 192-bit bus
- Memory bandwidth: 672 GB/s
- Board power: 250 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 5070?
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 | 164 to 218 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 82 to 109 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 80 to 107 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 54 to 72 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Tight | 45 to 59 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 5070
- Llama 3.1 8B and Qwen3 8B: about 7 GB at Q4 with 8K context; Q8_0 also fits (about 10.6 GB).
- Gemma 3 12B (Q4_K_M): about 9.3 GB.
- 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
gpt-oss-20b (about 14 GB) and larger models need more than 12 GB at Q4.
Fast, with a 12 GB limit. It is a good choice for 8B to 12B models. If you want to run 20B models, a 16 GB card is the safer pick.
Verdict
What speed to expect
At 672 GB/s, an 8B Q4 model has a ceiling near 137 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 82 to 109 tokens per second for Llama 3.1 8B and 54 to 72 for Gemma 3 12B. We did not find a reliable measured result for this exact card, so these are estimates that could be off by 10 to 20 percent.
Frequently asked questions
Can the RTX 5070 run a 14B model?
Barely. Qwen3 14B at Q4_K_M needs about 11.4 GB at 8K context.
Can it run gpt-oss-20b?
Not fully on the GPU: it needs about 14 GB.
5070 vs 4070 Super for AI?
Both have 12 GB. The 5070 has more bandwidth (672 vs 504 GB/s), so it should generate faster.
Sources and method
- RTX 5070 specs: PNY’s NVIDIA GeForce linecard, January 2026 (12 GB, GDDR7, 672 GB/s, 250 W).
- 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.
