What GPU do I need to run meta-llama/Llama-3.2-3B-Instruct?

What Llama-3.2-3B-Instruct is

Llama-3.2-3B-Instruct is a 3.2B-parameter language model published by Meta on Hugging Face. It is released under Llama 3.2 Community License Agreement.

License note: Meta's own commercial license, not OSI-approved open source; the repo is gated on Hugging Face, so you accept its terms there before downloading weights. Facts in this section are sourced from Llama-3.2-3B-Instruct's Hugging Face model card, not benchmarked by Aquanode.

What it's used for

• Chat assistants
• Instruction following
• Synthetic data generation

VRAM required & cheapest live GPU fit

Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.

A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.

INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Llama-3.2-3B-Instruct: KV cache by context length

The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.

Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 114,688 bytes at 16-bit.

Computed from the layer, head and window counts in the model's own configuration (unsloth/Llama-3.2-3B-Instruct), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.

Llama-3.2-3B-Instruct: common questions

How to run Llama-3.2-3B-Instruct

vllm serve meta-llama/Llama-3.2-3B-Instruct --tensor-parallel-size 1

ollama run llama3.2:3b

llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF

Deploy Llama-3.2-3B-Instruct on Aquanode

Aquanode has no one-click deploy template for Llama-3.2-3B-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.

1. Launch a bare GPU pod sized to the requirement above (1× RTX 4070 Super or larger).
2. Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
3. Run the command and connect to the resulting endpoint.

Launch a GPU pod

More Llama 3.2 models

• Llama-3.2-3B (3.2B, BF16)
• Llama-3.2-3B-Instruct-pythonic (3.2B, BF16)
• NetrAI-L3 (3.2B, F16)
• alloma-3B-Instruct (3.2B, BF16)
• svara-tts-v1 (3.3B, F32)

All 15 Llama 3.2 models: VRAM and GPU requirements

Lineage

Quantized and fine-tuned versions

• orpheus-3b-0.1-ft
• alloma-3B-Instruct
• Llama-3.2-3B-Instruct-pythonic
• NetrAI-L3

Also published as: unsloth/Llama-3.2-3B-Instruct. Identical copies of this model under another account.

Alternatives at this size

Other models for text-generation within about a third of Llama-3.2-3B-Instruct's 3.2B parameters, from other model lines.

• Qwen2.5-3B-Instruct (3.1B, BF16)
• phi-2 (2.8B, F16)
• PowerMoE-3b (3.4B, F32)
• gemma-2-2b-it (2.6B, BF16)
• SmolLM3-3B-Base (3.1B, BF16)

More on Llama-3.2-3B-Instruct

• Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.

• Best chat and assistants models: how Llama-3.2-3B-Instruct ranks against the rest.

• How to run Llama 3.2 and how to fine-tune Llama 3.2.

• Compare Llama-3.2-3B-Instruct with Qwen2.5-3B-Instruct, Qwen3-4B, Qwen3-4B-Instruct-2507, Qwen3-4B-Base, SmolLM3-3B-Base, Phi-3-mini-4k-instruct, SmolLM3-3B.