alpindale·Llama·LlamaForCausalLM

Llama Guard 3 1B — Hardware Requirements & GPU Compatibility

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Llama Guard 3 1B is a 1.5B-parameter open language model from alpindale in the Llama family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 1.27 GB of VRAM — see which GPUs and Macs can run it below.

305.5K downloads 2 likes 1.5K quant downloads131K context

Specifications

Publisher
alpindale
Family
Llama
Parameters
1.5B
Architecture
LlamaForCausalLM
Context Length
131,072 tokens
Vocabulary Size
128,256
Release Date
2024-09-25
License
llama3.2

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How Much VRAM Does Llama Guard 3 1B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.401 GB
Q3_K_S3.501.0 GB
Q3_K_M3.901.1 GB
Q4_04.001.1 GB
Q4_K_M4.801.3 GB
Q5_K_M5.701.4 GB
Q6_K6.601.6 GB
Q8_08.001.9 GB

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 Guard 3 1B?

Q4_K_M · 1.3 GB

Llama Guard 3 1B (Q4_K_M) requires 1.3 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 5.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom
NVIDIA GeForce RTX 5090~917 tok/sNVIDIA GeForce RTX 3090 Ti~516 tok/sNVIDIA GeForce RTX 4090~516 tok/sNVIDIA GeForce RTX 5080~491 tok/sNVIDIA GeForce RTX 3090~479 tok/sNVIDIA GeForce RTX 3080 Ti~467 tok/sNVIDIA GeForce RTX 5070 Ti~459 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~459 tok/sAMD Radeon RX 7900 XTX~454 tok/sNVIDIA GeForce RTX 3080~389 tok/sAMD Radeon RX 7900 XT~378 tok/sNVIDIA GeForce RTX 4080 SUPER~377 tok/sNVIDIA GeForce RTX 4080~367 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~344 tok/sNVIDIA GeForce RTX 5070~344 tok/sNVIDIA TITAN RTX~344 tok/sNVIDIA GeForce RTX 2080 Ti~315 tok/sNVIDIA GeForce RTX 3070 Ti~311 tok/sAMD Radeon RX 9070~302 tok/sAMD Radeon RX 9070 XT~302 tok/sAMD Radeon RX 7800 XT~295 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~295 tok/sAMD Radeon RX 7900 GRE~272 tok/sNVIDIA GeForce RTX 4070~258 tok/sNVIDIA GeForce RTX 4070 SUPER~258 tok/sNVIDIA GeForce RTX 4070 Ti~258 tok/sNVIDIA GeForce GTX 1080 Ti~248 tok/sAMD Radeon RX 6800~242 tok/sAMD Radeon RX 6800 XT~242 tok/sAMD Radeon RX 6900 XT~242 tok/sNVIDIA GeForce RTX 3060 Ti~229 tok/sNVIDIA GeForce RTX 3070~229 tok/sNVIDIA GeForce RTX 5060~229 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~229 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~229 tok/sIntel Arc A770 16GB~221 tok/sAMD Radeon RX 7700 XT~204 tok/sAMD Radeon RX 9070 GRE~204 tok/sIntel Arc A750~202 tok/sNVIDIA GeForce RTX 3060 12GB~184 tok/sAMD Radeon RX 6700 XT~181 tok/sIntel Arc B580~180 tok/sAMD Radeon RX 9060 XT 16GB~151 tok/sIntel Arc B570~150 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~147 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~147 tok/sNVIDIA GeForce RTX 4060~139 tok/sAMD Radeon RX 7600~136 tok/sAMD Radeon RX 7600 XT~136 tok/sAMD Radeon RX 9050~136 tok/sNVIDIA GeForce RTX 3060 8GB~123 tok/sNVIDIA GeForce RTX 3050 8GB~115 tok/s

Which Devices Can Run Llama Guard 3 1B?

Q4_K_M · 1.3 GB

59 devices with unified memory can run Llama Guard 3 1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

— Plenty of headroom
NVIDIA DGX H100~13717 tok/sNVIDIA DGX A100 640GB~8349 tok/sMac Studio (M3 Ultra, 256GB)~451 tok/sMac Studio (M3 Ultra, 512GB)~451 tok/sMac Studio (M3 Ultra, 96GB)~451 tok/sMac Pro M2 Ultra (192 GB)~441 tok/sMac Studio M2 Ultra (192 GB)~441 tok/sMacBook Pro 16" M5 Max (128 GB)~338 tok/sMac Studio M4 Max (128 GB)~301 tok/sMac Studio M4 Max (64 GB)~301 tok/sMacBook Pro 16" M4 Max (48 GB)~301 tok/sMacBook Pro 16" M4 Max (64 GB)~301 tok/sMac Studio M4 Max (36 GB)~226 tok/sMacBook Pro 14" M4 Max (36 GB)~226 tok/sMacBook Pro 16" M3 Max (48 GB)~226 tok/sMacBook Pro 14-inch (M5 Pro)~169 tok/sMac Mini M4 Pro (24 GB)~151 tok/sMac Mini M4 Pro (48 GB)~151 tok/sMacBook Pro 14" M4 Pro (24 GB)~151 tok/sMacBook Pro 16" M4 Pro (24 GB)~151 tok/sASUS Ascent GX10~140 tok/sNVIDIA DGX Spark~140 tok/sNVIDIA Jetson AGX Thor Developer Kit~140 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~131 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~131 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~131 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~131 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~131 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~131 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~131 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~117 tok/sNVIDIA Jetson AGX Orin 32GB~105 tok/sNVIDIA Jetson AGX Orin 64GB~105 tok/sMacBook Pro 14-inch (M5)~85 tok/siPad Pro M5 13" (16 GB)~84 tok/sSnapdragon X Elite Copilot+ PC~69 tok/sMac Mini M4 (16 GB)~66 tok/sMac Mini M4 (32 GB)~66 tok/sMacBook Air 13" M4 (16 GB)~66 tok/sMacBook Air 13" M4 (24 GB)~66 tok/sMacBook Air 15" M4 (16 GB)~66 tok/sMacBook Air 15" M4 (24 GB)~66 tok/sMacBook Pro 14" M4 (16 GB)~66 tok/siPad Pro M4 13" (16 GB)~66 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~57 tok/sMacBook Air 13" M3 (16 GB)~56 tok/sMacBook Air 13" M3 (24 GB)~56 tok/sMacBook Air 13" M3 (8 GB)~56 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~54 tok/sNVIDIA Jetson Orin NX 16GB~52 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~52 tok/sApple iPhone 17 Pro~42 tok/siPhone 17 Pro Max~42 tok/siPhone 17~38 tok/siPhone Air~38 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Where to Download Llama Guard 3 1B

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 Guard 3 1B need?

Llama Guard 3 1B requires 1.3 GB of VRAM at Q4_K_M, or 3.4 GB at BF16. Full 131K context adds up to 4.2 GB (5.5 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 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

1.3 GB
5.5 GB

Learn more about VRAM estimation →

What's the best quantization for Llama Guard 3 1B?

For Llama Guard 3 1B, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.8 GB.

VRAM requirement by quantization

IQ2_XXS
0.8 GB
IQ3_XS
1.0 GB
Q3_K_M
1.1 GB
Q4_K_M ★
1.3 GB
Q5_K_S
1.4 GB
BF16
3.4 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama Guard 3 1B on a Mac?

Llama Guard 3 1B requires at least 0.8 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 Guard 3 1B locally?

Yes — Llama Guard 3 1B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama Guard 3 1B?

At Q4_K_M, Llama Guard 3 1B can reach ~3780 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~516 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 ÷ 1.3 × 0.65 = ~4095 tok/s

Estimated speed at Q4_K_M (1.3 GB)

~4095 tok/s
~516 tok/s
~4095 tok/s
~3780 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Llama Guard 3 1B?

At Q4_K_M, the download is about 0.90 GB. The full-precision BF16 version is 3.00 GB. The smallest option (IQ2_XXS) is 0.41 GB.

Which GPUs can run Llama Guard 3 1B?

52 consumer GPUs can run Llama Guard 3 1B at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.

Which devices can run Llama Guard 3 1B?

59 devices with unified memory can run Llama Guard 3 1B at Q4_K_M (1.3 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.