NVIDIAAmpere

Best AI Models for NVIDIA GeForce RTX 3070 (8.0GB)

VRAM:8.0 GB GDDR6·Bandwidth:448.0 GB/s·CUDA Cores:5,888·TDP:220W·MSRP:$499

8 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.

With 8 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.

Runs Well

  • 3B–4B models at Q4–Q5 quality
  • 7B models at Q2–Q3 (usable but reduced quality)
  • Quick experiments and learning

Challenging

  • 7B models at Q4+ (VRAM too tight)
  • Any model above 7B parameters
  • Long context windows even with small models

What LLMs Can NVIDIA GeForce RTX 3070 Run?

18 models · 2 excellent · 7 good

Showing compatibility for NVIDIA GeForce RTX 3070

LLM models compatible with NVIDIA GeForce RTX 3070 — ranked by performance
ModelVRAMGrade
Q4_K_M·441.2 t/s tok/s·33K ctx·EASY RUN
0.7 GBD29
Q4_K_M·36.8 t/s tok/s·33K ctx·TOO HEAVY
7.9 GBD15

NVIDIA GeForce RTX 3070 Specifications

Brand
NVIDIA
Architecture
Ampere
VRAM
8.0 GB GDDR6
Memory Bandwidth
448.0 GB/s
CUDA Cores
5,888
Tensor Cores
184
FP16 Performance
40.60 TFLOPS
TDP
220W
Release Date
2020-10-29
MSRP
$499

Get Started

Ollama (Recommended)

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run llama3:8b

LM Studio

LM Studio

Download LM Studio, search for a model, and run it with one click.

GPUs to Consider Over NVIDIA GeForce RTX 3070

Similar GPUs and upgrades with more VRAM or higher bandwidth for AI

Frequently Asked Questions

Can NVIDIA GeForce RTX 3070 run Qwen3 8B?

Yes, the NVIDIA GeForce RTX 3070 with 8 GB can run Qwen3 8B, Llama 3.1 8B Instruct, Gemma 2 9B IT, and 666 other models. 55 models run at excellent quality, and 197 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.

Is NVIDIA GeForce RTX 3070 good for AI?

The NVIDIA GeForce RTX 3070 has 8 GB of GDDR6, making it usable for running local AI models. It supports 252 models at good quality or better. With 448.0 GB/s memory bandwidth, it delivers solid token generation speeds. You can run smaller models and experiment with quantized 7B models.

How many parameters can NVIDIA GeForce RTX 3070 handle?

With 8 GB, the NVIDIA GeForce RTX 3070 supports models from 1B to 7B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 13B parameters. Smaller 3B–7B models fit at Q3–Q4 quantization.

What quantization should I use on NVIDIA GeForce RTX 3070?

For the best balance of quality and speed on the NVIDIA GeForce RTX 3070, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. If a model barely fits, drop to Q3_K_M — quality loss is noticeable but still useful for chat. Avoid Q2_K unless you just want to test whether a model works at all.

How fast is NVIDIA GeForce RTX 3070 for AI inference?

With 448.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 3070 achieves approximately 65 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. Token generation speed scales inversely with model size — smaller models are significantly faster.

tok/s = (448 GB/s ÷ model GB) × efficiency

Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.

Estimated speed on NVIDIA GeForce RTX 3070

Real-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.

Learn more about tok/s estimation →

What's the best model for NVIDIA GeForce RTX 3070?

The top-rated models for the NVIDIA GeForce RTX 3070 are Qwen3 8B, Llama 3.1 8B Instruct, Gemma 2 9B IT. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.