Llama 3.2 3B Instruct GGUF — Hardware Requirements & GPU Compatibility
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This is a GGUF-quantized version of Meta's Llama 3.2 3B Instruct, repackaged by Bartowski. Llama 3.2 3B Instruct is part of Meta's lightweight Llama 3.2 release, optimized for on-device deployment and edge inference while delivering surprisingly capable instruction following and text generation. At 3 billion parameters, this model hits a sweet spot between size and capability. The GGUF format provided by Bartowski enables compatibility with llama.cpp-based tools, making it easy to run locally. It's an excellent choice for users who want a responsive, low-resource model for everyday tasks like summarization, Q&A, and general chat.
407.1K downloads
193 likesSpecifications
- Publisher
- Bartowski
- Family
- Llama 3
- Parameters
- 3B
- Release Date
- 2024-10-08
- License
- llama3.2
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HuggingFace
How Much VRAM Does Llama 3.2 3B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_M | 3.60 | 1.5 GB | — | 1.35 GB | Importance-weighted 3-bit, medium |
| Q4_0 | 4.00 | 1.6 GB | — | 1.50 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 1.7 GB | — | 1.54 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 1.8 GB | — | 1.61 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 1.9 GB | — | 1.69 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 2.0 GB | — | 1.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 2.0 GB | — | 1.84 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 2.3 GB | — | 2.06 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 2.4 GB | — | 2.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 2.4 GB | — | 2.17 GB | 5-bit large quantization |
| Q6_K | 6.60 | 2.7 GB | — | 2.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.3 GB | — | 3.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.2 3B Instruct GGUF?
Q4_K_M · 2.0 GBLlama 3.2 3B Instruct GGUF (Q4_K_M) requires 2.0 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 Instruct GGUF?
Q4_K_M · 2.0 GB33 devices with unified memory can run Llama 3.2 3B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama 3.2 3B Instruct GGUF need?
Llama 3.2 3B Instruct GGUF requires 2.0 GB of VRAM at Q4_K_M, or 3.3 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 3B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.0 GB- What's the best quantization for Llama 3.2 3B Instruct GGUF?
For Llama 3.2 3B Instruct GGUF, Q4_K_M (2.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.0 GB) provides better quality if you have the VRAM. The smallest option is IQ3_M at 1.5 GB.
VRAM requirement by quantization
IQ3_M1.5 GB~78%IQ4_XS1.8 GB~87%Q4_K_M ★2.0 GB~89%Q4_K_L2.0 GB~90%Q5_K_M2.4 GB~92%Q8_03.3 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 3B Instruct GGUF on a Mac?
Llama 3.2 3B Instruct GGUF requires at least 1.5 GB at IQ3_M, 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 Instruct GGUF locally?
Yes — Llama 3.2 3B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 2.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 3B Instruct GGUF?
At Q4_K_M, Llama 3.2 3B Instruct GGUF can reach ~1472 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~331 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 ÷ 2.0 × 0.55 = ~1472 tok/s
Estimated speed at Q4_K_M (2.0 GB)
AMD Instinct MI300X~1472 tok/sNVIDIA GeForce RTX 4090~331 tok/sNVIDIA H100 SXM~1100 tok/sAMD Instinct MI250X~910 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 Instruct GGUF?
At Q4_K_M, the download is about 1.80 GB. The full-precision Q8_0 version is 3.00 GB. The smallest option (IQ3_M) is 1.35 GB.