Llama 3.2 1B Instruct Uncensored — Hardware Requirements & GPU Compatibility
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Llama 3.2 1B Instruct Uncensored is a 1.2B-parameter open language model from nicoboss in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 1.11 GB of VRAM — see which GPUs and Macs can run it below.
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2.4K quant downloadsSpecifications
- Publisher
- nicoboss
- Family
- Llama 3
- Parameters
- 1.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-10-02
- License
- llama3.2
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HuggingFace
How Much VRAM Does Llama 3.2 1B Instruct Uncensored Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.9 GB | 5.1 GB | 0.53 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.9 GB | 5.1 GB | 0.54 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.0 GB | 5.2 GB | 0.60 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.0 GB | 5.2 GB | 0.62 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.1 GB | 5.3 GB | 0.74 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.3 GB | 5.5 GB | 0.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.4 GB | 5.6 GB | 1.02 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.6 GB | 5.8 GB | 1.24 GB | 8-bit quantization, near-lossless |
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 1B Instruct Uncensored?
Q4_K_M · 1.1 GBLlama 3.2 1B Instruct Uncensored (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 4.2 GB, bringing total usage to 5.3 GB. 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 1B Instruct Uncensored?
Q4_K_M · 1.1 GB59 devices with unified memory can run Llama 3.2 1B Instruct Uncensored, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Llama 3.2 1B Instruct Uncensored
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Llama 3.2 1B Instruct Uncensored need?
Llama 3.2 1B Instruct Uncensored requires 1.1 GB of VRAM at Q4_K_M, or 2.8 GB at BF16. Full 131K context adds up to 4.2 GB (5.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.1 GBQ4_K_M + full context5.3 GB- What's the best quantization for Llama 3.2 1B Instruct Uncensored?
For Llama 3.2 1B Instruct Uncensored, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.7 GB.
VRAM requirement by quantization
IQ2_XXS0.7 GBIQ3_XS0.9 GBQ3_K_M1.0 GBQ4_K_M ★1.1 GBQ5_K_S1.2 GBBF162.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 1B Instruct Uncensored on a Mac?
Llama 3.2 1B Instruct Uncensored requires at least 0.7 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 1B Instruct Uncensored locally?
Yes — Llama 3.2 1B Instruct Uncensored can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 1B Instruct Uncensored?
At Q4_K_M, Llama 3.2 1B Instruct Uncensored can reach ~3964 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~590 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 ÷ 1.1 × 0.65 = ~4685 tok/s
Estimated speed at Q4_K_M (1.1 GB)
~4685 tok/s~590 tok/s~4685 tok/s~3964 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 Uncensored?
At Q4_K_M, the download is about 0.74 GB. The full-precision BF16 version is 2.47 GB. The smallest option (IQ2_XXS) is 0.34 GB.
- Which GPUs can run Llama 3.2 1B Instruct Uncensored?
50 consumer GPUs can run Llama 3.2 1B Instruct Uncensored at Q4_K_M (1.1 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 1B Instruct Uncensored?
59 devices with unified memory can run Llama 3.2 1B Instruct Uncensored at Q4_K_M (1.1 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.