Meta·Llama 2

Llama 2 7B Chat HF — Hardware Requirements & GPU Compatibility

Chat

Meta Llama 2 7B Chat is a 7-billion parameter instruction-tuned model from Meta's Llama 2 family, optimized for dialogue use cases. It was fine-tuned using supervised fine-tuning and RLHF on top of the Llama 2 7B base model, with a 4K token context window. This model is suitable for basic conversational AI tasks and runs efficiently on consumer GPUs. While newer Llama generations offer improved performance, Llama 2 7B Chat remains a well-understood and widely-supported option for local inference. Released under the Llama 2 Community License.

258.1K downloads 4.8K likes 630 quant downloads

Specifications

Publisher
Meta
Family
Llama 2
Parameters
6.7B
Release Date
2023-07-13
License
Llama 2 Community

Get Started

How Much VRAM Does Llama 2 7B Chat HF Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.403.1 GB
Q3_K_S3.503.2 GB
Q3_K_M3.903.6 GB
Q4_K_M4.804.5 GB
Q5_K_M5.705.3 GB
Q6_K6.606.1 GB
Q8_08.007.4 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 7B Chat HF?

Q4_K_M · 4.5 GB

Llama 2 7B Chat HF (Q4_K_M) requires 4.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~262 tok/sNVIDIA GeForce RTX 3090 Ti~147 tok/sNVIDIA GeForce RTX 4090~147 tok/sNVIDIA GeForce RTX 5080~140 tok/sNVIDIA GeForce RTX 3090~137 tok/sNVIDIA GeForce RTX 3080 Ti~133 tok/sNVIDIA GeForce RTX 5070 Ti~131 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~131 tok/sAMD Radeon RX 7900 XTX~119 tok/sNVIDIA GeForce RTX 3080~111 tok/sNVIDIA GeForce RTX 4080 SUPER~108 tok/sNVIDIA GeForce RTX 4080~105 tok/sAMD Radeon RX 7900 XT~99 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~98 tok/sNVIDIA GeForce RTX 5070~98 tok/sNVIDIA TITAN RTX~98 tok/sNVIDIA GeForce RTX 2080 Ti~90 tok/sNVIDIA GeForce RTX 3070 Ti~89 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~84 tok/sAMD Radeon RX 9070~79 tok/sAMD Radeon RX 9070 XT~79 tok/sAMD Radeon RX 7800 XT~77 tok/sNVIDIA GeForce RTX 4070~74 tok/sNVIDIA GeForce RTX 4070 SUPER~74 tok/sNVIDIA GeForce RTX 4070 Ti~74 tok/sAMD Radeon RX 7900 GRE~71 tok/sNVIDIA GeForce GTX 1080 Ti~71 tok/sNVIDIA GeForce RTX 3060 Ti~65 tok/sNVIDIA GeForce RTX 3070~65 tok/sNVIDIA GeForce RTX 5060~65 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~65 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~65 tok/sAMD Radeon RX 6800~63 tok/sAMD Radeon RX 6800 XT~63 tok/sAMD Radeon RX 6900 XT~63 tok/sIntel Arc A770 16GB~63 tok/sIntel Arc A750~58 tok/sAMD Radeon RX 7700 XT~53 tok/sNVIDIA GeForce RTX 3060 12GB~53 tok/sIntel Arc B580~51 tok/sAMD Radeon RX 6700 XT~48 tok/sIntel Arc B570~43 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~42 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~42 tok/sNVIDIA GeForce RTX 4060~40 tok/sAMD Radeon RX 9060 XT 16GB~40 tok/sAMD Radeon RX 7600~36 tok/sAMD Radeon RX 7600 XT~36 tok/sNVIDIA GeForce RTX 3060 8GB~35 tok/sNVIDIA GeForce RTX 3050 8GB~33 tok/s

Which Devices Can Run Llama 2 7B Chat HF?

Q4_K_M · 4.5 GB

59 devices with unified memory can run Llama 2 7B Chat HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.

Runs great

Plenty of headroom
NVIDIA DGX H100~3915 tok/sNVIDIA DGX A100 640GB~2383 tok/sMac Studio (M3 Ultra, 256GB)~129 tok/sMac Studio (M3 Ultra, 512GB)~129 tok/sMac Studio (M3 Ultra, 96GB)~129 tok/sMac Pro M2 Ultra (192 GB)~126 tok/sMac Studio M2 Ultra (192 GB)~126 tok/sMacBook Pro 16" M5 Max (128 GB)~97 tok/sMac Studio M4 Max (128 GB)~86 tok/sMac Studio M4 Max (64 GB)~86 tok/sMacBook Pro 16" M4 Max (48 GB)~86 tok/sMacBook Pro 16" M4 Max (64 GB)~86 tok/sMac Studio M4 Max (36 GB)~64 tok/sMacBook Pro 14" M4 Max (36 GB)~64 tok/sMacBook Pro 16" M3 Max (48 GB)~64 tok/sMacBook Pro 14-inch (M5 Pro)~48 tok/sMac Mini M4 Pro (24 GB)~43 tok/sMac Mini M4 Pro (48 GB)~43 tok/sMacBook Pro 14" M4 Pro (24 GB)~43 tok/sMacBook Pro 16" M4 Pro (24 GB)~43 tok/sASUS Ascent GX10~40 tok/sNVIDIA DGX Spark~40 tok/sNVIDIA Jetson AGX Thor Developer Kit~40 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~37 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~37 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~37 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~37 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~37 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~37 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~37 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~33 tok/sNVIDIA Jetson AGX Orin 32GB~30 tok/sNVIDIA Jetson AGX Orin 64GB~30 tok/sMacBook Pro 14-inch (M5)~24 tok/siPad Pro M5 13" (16 GB)~24 tok/sSnapdragon X Elite Copilot+ PC~20 tok/sMac Mini M4 (16 GB)~19 tok/sMac Mini M4 (32 GB)~19 tok/sMacBook Air 13" M4 (16 GB)~19 tok/sMacBook Air 13" M4 (24 GB)~19 tok/sMacBook Air 15" M4 (16 GB)~19 tok/sMacBook Air 15" M4 (24 GB)~19 tok/sMacBook Pro 14" M4 (16 GB)~19 tok/siPad Pro M4 13" (16 GB)~19 tok/sMacBook Air 13" M3 (16 GB)~16 tok/sMacBook Air 13" M3 (24 GB)~16 tok/sMacBook Air 13" M3 (8 GB)~16 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~15 tok/sNVIDIA Jetson Orin NX 16GB~15 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~15 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~15 tok/sApple iPhone 17 Pro~12 tok/siPhone 17 Pro Max~12 tok/siPhone Air~11 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Decent

Enough memory, may be tight

Where to Download Llama 2 7B Chat HF

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 2 7B Chat HF need?

Llama 2 7B Chat HF requires 4.5 GB of VRAM at Q4_K_M, or 14.8 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 6.7B × 4.8 bits ÷ 8 = 4 GB

KV Cache + Overhead 0.5 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

4.5 GB

Learn more about VRAM estimation →

What's the best quantization for Llama 2 7B Chat HF?

For Llama 2 7B Chat HF, Q4_K_M (4.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.1 GB.

VRAM requirement by quantization

Q2_K
3.1 GB
Q3_K_L
3.8 GB
Q4_K_M
4.5 GB
Q5_K_S
5.1 GB
Q6_K
6.1 GB
BF16
14.8 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 2 7B Chat HF on a Mac?

Llama 2 7B Chat HF requires at least 3.1 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 7B Chat HF locally?

Yes — Llama 2 7B Chat HF can run locally on consumer hardware. At Q4_K_M quantization it needs 4.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama 2 7B Chat HF?

At Q4_K_M, Llama 2 7B Chat HF can reach ~989 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~147 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 ÷ 4.5 × 0.65 = ~1169 tok/s

Estimated speed at Q4_K_M (4.5 GB)

~1169 tok/s
~147 tok/s
~1169 tok/s
~989 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 7B Chat HF?

At Q4_K_M, the download is about 4.04 GB. The full-precision BF16 version is 13.48 GB. The smallest option (Q2_K) is 2.86 GB.

Which GPUs can run Llama 2 7B Chat HF?

50 consumer GPUs can run Llama 2 7B Chat HF at Q4_K_M (4.5 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 2 7B Chat HF?

59 devices with unified memory can run Llama 2 7B Chat HF at Q4_K_M (4.5 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.