Llama 3.2 3B Instruct — Hardware Requirements & GPU Compatibility
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947 downloads
9 likesSpecifications
- Publisher
- MLX Community
- Family
- Llama 3
- Parameters
- 3.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-09-25
- License
- llama3.2
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HuggingFace
How Much VRAM Does Llama 3.2 3B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.9 GB | 16.7 GB | 1.37 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.9 GB | 16.7 GB | 1.41 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.1 GB | 16.9 GB | 1.57 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.1 GB | 16.9 GB | 1.61 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.5 GB | 17.3 GB | 1.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.8 GB | 17.6 GB | 2.29 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.2 GB | 18.0 GB | 2.65 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.8 GB | 18.6 GB | 3.21 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.2 3B Instruct?
Q4_K_M · 2.5 GBLlama 3.2 3B Instruct (Q4_K_M) requires 2.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 14.8 GB, bringing total usage to 17.3 GB. 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?
Q4_K_M · 2.5 GB33 devices with unified memory can run Llama 3.2 3B Instruct, 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 need?
Llama 3.2 3B Instruct requires 2.5 GB of VRAM at Q4_K_M, or 3.8 GB at Q8_0. Full 131K context adds up to 14.8 GB (17.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.2B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.4 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M2.5 GBQ4_K_M + full context17.3 GB- What's the best quantization for Llama 3.2 3B Instruct?
For Llama 3.2 3B Instruct, Q4_K_M (2.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.4 GB.
VRAM requirement by quantization
IQ2_XXS1.4 GB~53%IQ3_M2.0 GB~78%IQ4_NL2.3 GB~88%Q4_K_M ★2.5 GB~89%Q4_K_L2.5 GB~90%Q8_03.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 3B Instruct on a Mac?
Llama 3.2 3B Instruct requires at least 1.4 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 Instruct locally?
Yes — Llama 3.2 3B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 3B Instruct?
At Q4_K_M, Llama 3.2 3B Instruct can reach ~1185 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~266 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 ÷ 2.5 × 0.55 = ~1185 tok/s
Estimated speed at Q4_K_M (2.5 GB)
AMD Instinct MI300X~1185 tok/sNVIDIA GeForce RTX 4090~266 tok/sNVIDIA H100 SXM~886 tok/sAMD Instinct MI250X~733 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?
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.
- Which GPUs can run Llama 3.2 3B Instruct?
35 consumer GPUs can run Llama 3.2 3B Instruct at Q4_K_M (2.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.2 3B Instruct?
33 devices with unified memory can run Llama 3.2 3B Instruct at Q4_K_M (2.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.