Home Llama 3.2 3B — Hardware Requirements & GPU Compatibility
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Home Llama 3.2 3B is a 3.2B-parameter open language model from acon96 in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 2.46 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
11 likesSpecifications
- Publisher
- acon96
- Family
- Llama 3
- Parameters
- 3.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-06-05
- License
- Other
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HuggingFace
How Much VRAM Does Home Llama 3.2 3B 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_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 Home Llama 3.2 3B?
Q4_K_M · 2.5 GBHome Llama 3.2 3B (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 Home Llama 3.2 3B?
Q4_K_M · 2.5 GB33 devices with unified memory can run Home Llama 3.2 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Home Llama 3.2 3B need?
Home Llama 3.2 3B 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 Home Llama 3.2 3B?
For Home Llama 3.2 3B, Q4_K_M (2.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.7 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 1.9 GB.
VRAM requirement by quantization
IQ3_XS1.9 GBQ3_K_S1.9 GBIQ4_XS2.3 GBQ4_K_M ★2.5 GBQ5_K_S2.7 GBQ8_03.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Home Llama 3.2 3B on a Mac?
Home Llama 3.2 3B requires at least 1.9 GB at IQ3_XS, 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 Home Llama 3.2 3B locally?
Yes — Home Llama 3.2 3B 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 Home Llama 3.2 3B?
At Q4_K_M, Home Llama 3.2 3B 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)
~1185 tok/s~266 tok/s~886 tok/s~733 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Home 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 (IQ3_XS) is 1.33 GB.
- Which GPUs can run Home Llama 3.2 3B?
35 consumer GPUs can run Home Llama 3.2 3B 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 Home Llama 3.2 3B?
33 devices with unified memory can run Home Llama 3.2 3B 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.