Llama 3.2 3B — Hardware Requirements & GPU Compatibility
Chat
Meta Llama 3.2 3B is a 3.2-billion parameter base (pretrained) model from Meta's Llama 3.2 family. It supports a 128K token context window and is intended for fine-tuning, research, and custom applications rather than direct conversational use. The model provides a good balance between capability and efficiency at the small model scale. It is popular as a foundation for community fine-tunes and domain-specific adaptations. Released under the Llama 3.2 Community License.
1.6M downloads
705 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 3.2B
- Release Date
- 2024-10-24
- License
- llama3.2
Get Started
HuggingFace
How Much VRAM Does Llama 3.2 3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 1.0 GB | — | 0.88 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 1.2 GB | — | 1.08 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 1.4 GB | — | 1.24 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 1.5 GB | — | 1.37 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.6 GB | — | 1.41 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 1.6 GB | — | 1.45 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 1.7 GB | — | 1.57 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.8 GB | — | 1.61 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 1.8 GB | — | 1.65 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 1.9 GB | — | 1.73 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 2.0 GB | — | 1.81 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 2.0 GB | — | 1.81 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 2.0 GB | — | 1.81 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 2.1 GB | — | 1.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 2.2 GB | — | 1.97 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 2.4 GB | — | 2.21 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 2.5 GB | — | 2.29 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 2.6 GB | — | 2.33 GB | 5-bit large quantization |
| Q6_K | 6.60 | 2.9 GB | — | 2.65 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | — | 3.21 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.2 3B?
Q4_K_M · 2.1 GBLlama 3.2 3B (Q4_K_M) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llama 3.2 3B?
Q4_K_M · 2.1 GB33 devices with unified memory can run Llama 3.2 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Llama 3.2 3B need?
Llama 3.2 3B requires 2.1 GB of VRAM at Q4_K_M, or 3.5 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 3.2B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.1 GB- What's the best quantization for Llama 3.2 3B?
For Llama 3.2 3B, Q4_K_M (2.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.0 GB.
VRAM requirement by quantization
IQ2_XXS1.0 GB~53%IQ3_M1.6 GB~78%IQ4_NL2.0 GB~88%Q4_K_M ★2.1 GB~89%Q4_K_L2.2 GB~90%Q8_03.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 3B on a Mac?
Llama 3.2 3B requires at least 1.0 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 3.2 3B locally?
Yes — Llama 3.2 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 3B?
At Q4_K_M, Llama 3.2 3B can reach ~1375 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~309 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.1 × 0.55 = ~1375 tok/s
Estimated speed at Q4_K_M (2.1 GB)
AMD Instinct MI300X~1375 tok/sNVIDIA GeForce RTX 4090~309 tok/sNVIDIA H100 SXM~1028 tok/sAMD Instinct MI250X~850 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.2 3B?
At Q4_K_M, the download is about 1.93 GB. The full-precision Q8_0 version is 3.21 GB. The smallest option (IQ2_XXS) is 0.88 GB.