Meta·Llama 2

Llama 2 13B Chat HF — Hardware Requirements & GPU Compatibility

Chat

Meta Llama 2 13B Chat is a 13-billion parameter instruction-tuned model from Meta's Llama 2 family, fine-tuned for dialogue and chat applications. It offers improved reasoning and generation quality over the 7B variant while maintaining manageable hardware requirements with a 4K token context window. The model was fine-tuned using supervised fine-tuning and RLHF. It can run on consumer GPUs with 16GB or more of VRAM at reduced precision. Released under the Llama 2 Community License.

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Specifications

Publisher
Meta
Family
Llama 2
Parameters
13.0B
Release Date
2023-07-13
License
Llama 2 Community

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How Much VRAM Does Llama 2 13B Chat HF Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.406.1 GB
Q3_K_Mest.3.907.0 GB
Q4_K_Mest.4.808.6 GB
Q5_K_Mest.5.7010.2 GB
Q6_Kest.6.6011.8 GB
Q8_0est.8.0014.3 GB
BF16est.16.0028.6 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 2 13B Chat HF?

Q4_K_M · 8.6 GB

Llama 2 13B Chat HF (Q4_K_M) requires 8.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.

Which Devices Can Run Llama 2 13B Chat HF?

Q4_K_M · 8.6 GB

49 devices with unified memory can run Llama 2 13B Chat HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).

Runs great

Plenty of headroom
NVIDIA DGX H100~2028 tok/sNVIDIA DGX A100 640GB~1234 tok/sMac Studio (M3 Ultra, 256GB)~67 tok/sMac Studio (M3 Ultra, 512GB)~67 tok/sMac Studio (M3 Ultra, 96GB)~67 tok/sMac Pro M2 Ultra (192 GB)~65 tok/sMac Studio M2 Ultra (192 GB)~65 tok/sMacBook Pro 16" M5 Max (128 GB)~50 tok/sMac Studio M4 Max (128 GB)~45 tok/sMac Studio M4 Max (64 GB)~45 tok/sMacBook Pro 16" M4 Max (48 GB)~45 tok/sMacBook Pro 16" M4 Max (64 GB)~45 tok/sMac Studio M4 Max (36 GB)~33 tok/sMacBook Pro 14" M4 Max (36 GB)~33 tok/sMacBook Pro 16" M3 Max (48 GB)~33 tok/sMacBook Pro 14-inch (M5 Pro)~25 tok/sMac Mini M4 Pro (24 GB)~22 tok/sMac Mini M4 Pro (48 GB)~22 tok/sMacBook Pro 14" M4 Pro (24 GB)~22 tok/sMacBook Pro 16" M4 Pro (24 GB)~22 tok/sASUS Ascent GX10~21 tok/sNVIDIA DGX Spark~21 tok/sNVIDIA Jetson AGX Thor Developer Kit~21 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~19 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~19 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~19 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~19 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~19 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~19 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~19 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~17 tok/sNVIDIA Jetson AGX Orin 32GB~16 tok/sNVIDIA Jetson AGX Orin 64GB~16 tok/sMacBook Pro 14-inch (M5)~13 tok/sSnapdragon X Elite Copilot+ PC~10 tok/sMac Mini M4 (16 GB)~10 tok/sMac Mini M4 (32 GB)~10 tok/sMacBook Air 13" M4 (16 GB)~10 tok/sMacBook Air 13" M4 (24 GB)~10 tok/sMacBook Air 15" M4 (16 GB)~10 tok/sMacBook Air 15" M4 (24 GB)~10 tok/sMacBook Pro 14" M4 (16 GB)~10 tok/siPad Pro M4 13" (16 GB)~10 tok/sMacBook Air 13" M3 (16 GB)~8 tok/sMacBook Air 13" M3 (24 GB)~8 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~8 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~8 tok/sNVIDIA Jetson Orin NX 16GB~8 tok/s

Decent

Enough memory, may be tight

Related Models

Frequently Asked Questions

How much VRAM does Llama 2 13B Chat HF need?

Llama 2 13B Chat HF requires 8.6 GB of VRAM at Q4_K_M, or 28.6 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 13.0B × 4.8 bits ÷ 8 = 7.8 GB

KV Cache + Overhead 0.8 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

8.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Llama 2 13B Chat HF?

Yes, at Q8_0 (14.3 GB) or lower. Higher quantizations like BF16 (28.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Llama 2 13B Chat HF?

For Llama 2 13B Chat HF, Q4_K_M (8.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.1 GB.

VRAM requirement by quantization

Q2_K
6.1 GB
Q4_K_M
8.6 GB
Q5_K_M
10.2 GB
Q6_K
11.8 GB
Q8_0
14.3 GB
BF16
28.6 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 2 13B Chat HF on a Mac?

Llama 2 13B Chat HF requires at least 6.1 GB at Q2_K, 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 2 13B Chat HF locally?

Yes — Llama 2 13B Chat HF can run locally on consumer hardware. At Q4_K_M quantization it needs 8.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama 2 13B Chat HF?

At Q4_K_M, Llama 2 13B Chat HF can reach ~512 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~76 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 B2008000 ÷ 8.6 × 0.65 = ~605 tok/s

Estimated speed at Q4_K_M (8.6 GB)

~605 tok/s
~76 tok/s
~605 tok/s
~512 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 2 13B Chat HF?

At Q4_K_M, the download is about 7.81 GB. The full-precision BF16 version is 26.03 GB. The smallest option (Q2_K) is 5.53 GB.

Which GPUs can run Llama 2 13B Chat HF?

39 consumer GPUs can run Llama 2 13B Chat HF at Q4_K_M (8.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.

Which devices can run Llama 2 13B Chat HF?

52 devices with unified memory can run Llama 2 13B Chat HF at Q4_K_M (8.6 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.