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URL: https://willitrunai.com/models/hf-bartowski--llama-3-2-3b-instruct-gguf

⇱ Llama 3.2 3B Instruct VRAM Requirements — GPU Compatibility


Bartowski

Llama 3.2 3B Instruct

👁 huggingface
HuggingFace
Limited data available — some specs may be incomplete or estimated.
407.1KDownloads193Likes0K tokensContextUnknownLicense5 EntryQuality

Llama 3.2 3B Instruct (3B parameters) requires approximately 4.3 GB of VRAM with Q5_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 5 GB of VRAM.

Get started

— copy & paste to run locally

Copy-paste commands to run Llama 3.2 3B Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "bartowski/Llama-3.2-3B-Instruct-GGUF" \ --hf-file "Llama-3.2-3B-Instruct-GGUF-Q5_K_M.gguf" \ -c 4096 -ngl 99

Quick specs

Parameters3B
Architecturedense
Context0K tokens
Modalitytext
Min RAM1.2 GB
Rec. RAM2.2 GB (Q5_K_M)
LicenseUnknown
FamilyLlama
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Related models

Your hardware

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Quick picks

👁 Intel
Best budgetC
Intel Arc A380 6GB~$139 — 42 tok/s
👁 NVIDIA
Best overallC
RTX 2060 6GB~$349 — 42 tok/s

Best hardware

Top picks for Llama 3.2 3B Instruct

RTX 2060 6GBC
6 GB
RTX 4050 Laptop 6GBC
6 GB
GTX 1060 6GBC
6 GB
GTX 1660 Super 6GBC
6 GB
GTX 1660 Ti 6GBC
6 GB

Run this model

Llama 3.2 3B Instruct on RTX 2060 6GBLlama 3.2 3B Instruct on RTX 4050 Laptop 6GBLlama 3.2 3B Instruct on GTX 1060 6GB

Quantization options

VRAM estimates by quant level

No hardware detected — fit column shows raw VRAM estimates

QuantBitsVRAMQualityFit
Q2_K
2
1.2 GB
Low
Q3_K_S
3
1.5 GB
Low
NVFP4
4
1.7 GB
Medium
Q4_K_M
4
1.8 GB
Medium
Q5_K_M
5
2.2 GB
High
Q6_K
6
2.5 GB
High
Q8_0
8
3.2 GB
Very High
F16
16
6.1 GB
Maximum

Hardware compatibility

Fit estimates across all hardware

Open calculator

Computing compatibility...

Memory breakdown

Reference: RTX 2060 6GB

Weights2.2 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom0.6 GB

Frequently asked questions

FAQ — Llama 3.2 3B Instruct

See also

Quantization GuideScoring MethodologyVRAM Calculator