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.
817.3K downloads
814 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 3.2B
- Release Date
- 2024-09-18
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.5 GB | — | 1.37 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.7 GB | — | 1.57 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 2.1 GB | — | 1.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.5 GB | — | 2.29 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 2.9 GB | — | 2.65 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 3.5 GB | — | 3.21 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 7.1 GB | — | 6.43 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
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. 50 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 GB59 devices with unified memory can run Llama 3.2 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
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 7.1 GB at BF16.
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. Q5_K_M (2.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.5 GB.
VRAM requirement by quantization
Q2_K1.5 GBQ4_K_M ★2.1 GBQ5_K_M2.5 GBQ6_K2.9 GBQ8_03.5 GBBF167.1 GB★ 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 1.5 GB at Q2_K, 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 ~2076 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 2.1 × 0.65 = ~2453 tok/s
Estimated speed at Q4_K_M (2.1 GB)
~2453 tok/s~309 tok/s~2453 tok/s~2076 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 BF16 version is 6.43 GB. The smallest option (Q2_K) is 1.37 GB.
- Which GPUs can run Llama 3.2 3B?
50 consumer GPUs can run Llama 3.2 3B at Q4_K_M (2.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.2 3B?
59 devices with unified memory can run Llama 3.2 3B at Q4_K_M (2.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.