Question 1

Does Llama 3.3 require an internet connection?

Accepted Answer

No. Once downloaded, Llama 3.3 runs entirely locally on your hardware, ensuring data privacy and offline availability.

Question 2

Can I use Llama 3.3 commercially?

Accepted Answer

Yes, Meta allows commercial use of Llama 3.3 for organizations with fewer than 700 million monthly active users under the Meta Llama License.

Question 3

Is Llama 3.3 better than GPT-4?

Accepted Answer

Llama 3.3 70B is competitive with GPT-4 in many benchmarks. It outperforms GPT-4 on several reasoning and math benchmarks while being free to deploy privately.

Question 4

How much VRAM does Llama 3.3 need?

Accepted Answer

Running Llama 3.3 70B at Q4_K_M quantization requires approximately 40-45GB of VRAM. For a single GPU, the RTX 5090 (32GB) can handle it with minor CPU offloading.

Question 5

What is the fastest way to run Llama 3.3 locally?

Accepted Answer

The fastest single-GPU setup is an RTX 5090 with 32GB GDDR7 and 1,792 GB/s bandwidth. Using llama.cpp or vLLM with FlashAttention 2 enables maximum tokens-per-second.

Question 6

Is Llama 3.3 good for coding?

Accepted Answer

Yes. Llama 3.3 has strong instruction following and code generation capabilities, competing with GPT-4o in standard HumanEval benchmarks.

Question 7

How many parameters does Llama 3.3 have?

Accepted Answer

The flagship version is 70B parameters. Meta also released variants at 8B and 405B (Llama 3.1), but Llama 3.3 specifically focuses on the 70B sweet spot for local performance.

Question 8

Can Llama 3.3 run on a single GPU?

Accepted Answer

Yes, with quantization — Q4_K_M reduces the model to ~40GB. An RTX 5090 (32GB) handles this with partial CPU offloading. For full unquantized inference, dual high-VRAM GPUs are recommended.

The Best GPUs for Running Llama 3.3

Recommended VRAM Configurations

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