The RTX 3070 has 8 GB of GDDR6 and 448 GB/s of bandwidth. That is far more speed than an RTX 4060 (272 GB/s), but the same 8 GB limit, so it can run 8B models quickly and very little beyond them.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 3070 specs that matter for local AI
- Memory: 8 GB GDDR6 on a 256-bit bus
- Memory bandwidth: 448 GB/s
- Bus width: 256-bit
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 3070?
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 8 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 109 to 146 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 55 to 73 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 54 to 71 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | No | – |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | No | – |
| 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 3070
- Llama 3.1 8B and Qwen3 8B (Q4_K_M): about 7 GB at 8K context, so the fit is snug.
- Qwen3 4B: about 4.7 GB, with plenty of room for long context.
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 8 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 | No |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | No |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | No |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | No |
| 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
Q6 and Q8 versions of 8B models, and all models of 12B and above, exceed 8 GB once context is added.
Fast, but capped at 8 GB. It is a good card for 8B models. For anything larger, VRAM is the constraint.
Verdict
What speed to expect
A community llama.cpp dataset (May 2024) measured the RTX 3070 at 72.8 tokens per second on Llama 3 8B Q4_K_M, falling to 61.6 at 8K context. FP16 ran out of memory. Our bandwidth-based estimate is 55 to 73 tokens per second, and the measured result is at the top of that range.
Frequently asked questions
Can the RTX 3070 run Llama 3 8B?
Yes, at Q4_K_M, with about 7 GB used at 8K context.
RTX 3070 vs RTX 3060 12GB for AI?
The 3070 is faster; the 3060 12GB fits larger models like Gemma 3 12B.
How fast is it?
About 73 tokens per second on Llama 3 8B Q4_K_M in a community dataset.
Sources and method
- RTX 3070 specs: Guru3D’s RTX 3060 Ti launch article (14 Gbps, 256-bit, 448 GB/s) and Digital Trends’ 3060 vs 3060 Ti vs 3070 comparison (8 GB, 256-bit).
- 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.
