Llama 3.2 1B Instruct GGUF — Hardware Requirements & GPU Compatibility
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This is a GGUF-quantized version of Meta's Llama 3.2 1B Instruct, repackaged by Bartowski. At just 1 billion parameters, Llama 3.2 1B Instruct is Meta's smallest instruction-tuned model, purpose-built for ultra-lightweight deployment on edge devices and resource-constrained hardware. The GGUF format from Bartowski makes this tiny model compatible with llama.cpp and its ecosystem. While it won't match larger models on complex reasoning, it excels at simple tasks like text classification, basic Q&A, and short-form generation. Its minimal resource requirements mean it can run on almost anything, making it ideal for experimentation or always-on local assistants on low-power hardware.
100.1K downloads
156 likesSpecifications
- Publisher
- Bartowski
- Family
- Llama 3
- Parameters
- 1B
- Release Date
- 2024-10-08
- License
- llama3.2
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HuggingFace
How Much VRAM Does Llama 3.2 1B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_M | 3.60 | 0.5 GB | — | 0.45 GB | Importance-weighted 3-bit, medium |
| Q4_0 | 4.00 | 0.6 GB | — | 0.50 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 0.6 GB | — | 0.51 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.6 GB | — | 0.54 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 0.6 GB | — | 0.56 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 0.7 GB | — | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 0.7 GB | — | 0.61 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 0.8 GB | — | 0.69 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.8 GB | — | 0.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 0.8 GB | — | 0.72 GB | 5-bit large quantization |
| Q6_K | 6.60 | 0.9 GB | — | 0.82 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.1 GB | — | 1.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.2 1B Instruct GGUF?
Q4_K_M · 0.7 GBLlama 3.2 1B Instruct GGUF (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 1B Instruct GGUF?
Q4_K_M · 0.7 GB33 devices with unified memory can run Llama 3.2 1B 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 1B Instruct GGUF need?
Llama 3.2 1B Instruct GGUF requires 0.7 GB of VRAM at Q4_K_M, or 1.1 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.7 GB- What's the best quantization for Llama 3.2 1B Instruct GGUF?
For Llama 3.2 1B Instruct GGUF, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.7 GB) provides better quality if you have the VRAM. The smallest option is IQ3_M at 0.5 GB.
VRAM requirement by quantization
IQ3_M0.5 GB~78%IQ4_XS0.6 GB~87%Q4_K_M ★0.7 GB~89%Q4_K_L0.7 GB~90%Q5_K_M0.8 GB~92%Q8_01.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 1B Instruct GGUF on a Mac?
Llama 3.2 1B Instruct GGUF requires at least 0.5 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 1B Instruct GGUF locally?
Yes — Llama 3.2 1B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 1B Instruct GGUF?
At Q4_K_M, Llama 3.2 1B Instruct GGUF can reach ~4417 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~993 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 ÷ 0.7 × 0.55 = ~4417 tok/s
Estimated speed at Q4_K_M (0.7 GB)
AMD Instinct MI300X~4417 tok/sNVIDIA GeForce RTX 4090~993 tok/sNVIDIA H100 SXM~3301 tok/sAMD Instinct MI250X~2731 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 1B Instruct GGUF?
At Q4_K_M, the download is about 0.60 GB. The full-precision Q8_0 version is 1.00 GB. The smallest option (IQ3_M) is 0.45 GB.