The RTX 4070 Ti Super is a sweet spot for local AI: 16 GB of GDDR6X and 672 GB/s of bandwidth. The 256-bit bus matters because generation speed is bandwidth-bound.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 4070 Ti Super specs that matter for local AI
- Memory: 16 GB GDDR6X on a 256-bit bus
- Memory bandwidth: 672 GB/s
- Board power: 285 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 Ti 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 16 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 | Fits | 45 to 59 tok/s |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | Fits | Varies (MoE) |
| 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 Ti Super
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K, with room for context.
- gpt-oss-20b: about 14 GB total.
- Gemma 3 12B and 8B models: comfortable at Q4, and Q8_0 also fits.
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 16 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 | Fits |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | Fits |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | Tight |
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
24B to 32B models at Q4 need 17 to 23 GB, so they exceed 16 GB. Models of that class need a 24 GB card.
Good value for 16 GB. It runs everything up to the 20B class at a comfortable speed. If you need to run 24B+ models, step up to a 24 GB card.
Verdict
What speed to expect
At 672 GB/s an 8B Q4 model has a ceiling near 137 tokens per second, so expect about 82 to 109 tokens per second, and 45 to 60 for a 14B model. We did not find a reliable measured result for this exact card. The RTX 4070 Ti (504 GB/s) was measured at 83.5 tokens per second on Llama 3 8B Q4_K_M in a community dataset from May 2024, which fits the bandwidth scaling.
Frequently asked questions
Can the RTX 4070 Ti Super run a 20B model?
Yes. gpt-oss-20b needs about 14 GB in total at 8K context.
Does 256-bit memory help for AI?
Yes. A wider bus gives more bandwidth, and bandwidth sets generation speed.
Is it better than the RTX 4060 Ti 16GB?
For speed, yes: 672 GB/s vs 288 GB/s. Both have 16 GB.
Sources and method
- RTX 4070 Ti Super specs: GamersNexus’ RTX 4080 Super, 4070 Ti Super and 4070 Super specs.
- 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.
