The RTX 5070 Ti offers 16 GB of GDDR7 and 896 GB/s of bandwidth. Compared with the 5060 Ti 16GB it has the same memory size but twice the bandwidth, so the same models run roughly twice as fast.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 5070 Ti specs that matter for local AI
- Memory: 16 GB GDDR7 on a 256-bit bus
- Memory bandwidth: 896 GB/s
- Board power: 300 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 Ti?
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 | 218 to 291 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 109 to 146 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 107 to 143 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 72 to 96 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Fits | 59 to 79 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 5070 Ti
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K, with room for context.
- gpt-oss-20b: about 14 GB total.
- 8B and 12B models: Q8_0 of an 8B model fits (about 10.6 GB at 8K).
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
Mistral Small 3.2 24B (about 17 GB), the 27B models (about 19 GB) and Qwen3 32B (about 23 GB) need more than 16 GB at Q4.
Fast for its memory size. It is a great fit for 8B to 20B models. If you want 24B and larger, you need 24 GB.
Verdict
What speed to expect
At 896 GB/s, an 8B Q4 model has a ceiling near 182 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 109 to 146 tokens per second for Llama 3.1 8B and 72 to 96 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 Ti run gpt-oss-20b?
Yes, with about 14 GB used at 8K context.
5070 Ti vs 5060 Ti 16GB for AI?
Same 16 GB, but 896 vs 448 GB/s, so the 5070 Ti should generate about twice as fast.
Can it run a 27B model?
Not at Q4: it needs about 19 GB at 8K context.
Sources and method
- RTX 5070 Ti specs: PNY’s NVIDIA GeForce linecard, January 2026 (16 GB, GDDR7, 896 GB/s, 300 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.
