Llama 3.2 3B VRAM: what it needs at Q4_K_M, Q8_0 and FP16
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KV cache is counted at FP16. Overhead is a fixed 1.5 GiB estimate for the runtime and compute buffers. A custom size borrows the architecture of the nearest preset.
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GPU tiers
- 8 GiBfitsRTX 4060, RTX 3070, RX 7600
- 12 GiBfitsRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBfitsRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBfitsRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBfitsRTX 5090, V100 32GB
- 48 GiBfitsRTX A6000, RTX 6000 Ada, L40S
- 80 GiBfitsA100 80GB, H100 80GB
At Q4_K_M with an 8,192-token context, Llama 3.2 3B needs about 4.08 GiB of VRAM: 1.71 GiB of weights, 0.88 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 8 GiB (RTX 4060, RTX 3070, RX 7600).
How much VRAM Llama 3.2 3B needs
Llama 3.2 3B is the larger of Meta's two lightweight text models from September 2024, built for on-device assistants, summarising and tool-calling where a phone, laptop or small GPU has to carry the whole model.
With 8,192 tokens of context the estimate is 7.96 GiB at FP16, 5.34 GiB at Q8_0 and 4.08 GiB at Q4_K_M. FP16 needs an 8 GiB card, Q8_0 needs an 8 GiB card, and Q4_K_M needs an 8 GiB card. The weights are 3 billion parameters times the bits per weight of each format (16, 8.5 and 4.89), divided by eight; everything is in GiB, the unit GPU memory is sold in.
Context length and the KV cache
The KV cache stores a key and a value vector for every token in every layer. Llama 3.2 3B has 28 layers, 8 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 112 KiB. That is 0.88 GiB at 8,192 tokens, and 14.0 GiB at the maximum of 131,072, where the Q4_K_M total becomes 17.2 GiB and needs a 24 GiB card. The cache does not shrink with weight quantization; only a runtime option that quantizes the cache itself does.
Which quantization to pick
At this size quantization artefacts show up sooner than on big models, so Q8_0 at 5.34 GiB is the sensible default on any card with 8 GiB, and even FP16 at 7.96 GiB fits an 8 GiB card. Reach for Q4_K_M only for phones and integrated graphics.
Pitfalls
The trap is the context, not the weights: at the full 131,072 tokens the KV cache alone is 14.0 GiB, several times the 1.71 GiB of Q4_K_M weights. On a Mac, Apple Silicon lets the GPU use only about three quarters of unified memory by default, so an 8 GB machine has roughly 6 GB to work with.
For more capability on the same card, compare Llama 3.1 8B and Qwen2.5 7B.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Llama 3.2 3B run on a 4 GB GPU?
Only at a short context. Q4_K_M weights are 1.71 GiB, and with an 8,192-token cache plus 1.5 GiB of overhead the estimate is 4.08 GiB, which is just over 4 GiB. Dropping to 4,096 tokens brings it to 3.65 GiB.
How much memory does Llama 3.2 3B need for its full 128k context?
At Q4_K_M with 131,072 tokens the estimate is 17.2 GiB, of which 14.0 GiB is KV cache, so it needs a 24 GiB card.
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Estimates only; the KV cache is sized at FP16 and the overhead is a fixed allowance, so always confirm against your own runtime’s report before buying hardware. OpsCanopy is free and open.