Llama 2 7B — Hardware Requirements & GPU Compatibility
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Llama 2 7B is a 7B-parameter open language model from Meta in the Llama 2 family. At Q4_K_M it needs about 4.62 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~142 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~30 tok/s)
Llama 2 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 2 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve meta-llama/Llama-2-7b --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~51 tok/s
- generation speed
- Cost per 1M output tokens
- $0.44
- per 1M output tokens
How Much VRAM Does Llama 2 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.3 GB | 8.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~200 tok/s | |
| 3.8 GB | 9.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~175 tok/s | |
| 4.6 GB | 9.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~142 tok/s | |
| 5.5 GB | 10.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~119 tok/s | |
| 6.3 GB | 11.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~103 tok/s | |
| 7.7 GB | 13 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~85 tok/s | |
| 15.4 GB | 20.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~43 tok/s |
Selected Q4_K_M
4-bit medium quantization7.1GBat 16K context
- Weights
- 4.0 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.9 GB free · ~142 tok/s
≥50 tok/s needs ≥355 GB/s · RTX 5090 ~252, RTX 3090 Ti ~142
Which Hardware Can Run Llama 2 7B?
Q4_K_M · 4.6 GBLlama 2 7B (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Llama 2 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.9 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~107Simulate output on RTX 3080RTX 2080 Ti~87Simulate output on RTX 2080 TiRTX 3070 Ti~86Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 12.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~104Simulate output on RX 7900 XT32–40 GB
fits · 24.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~252Simulate output on RTX 5090M5 Max36 GB~70Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~29Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 2 7B
Llama 2 family · 10 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 7B
- Release Date
- 2023-07-09
- License
- Llama 2 Community
Frequently Asked Questions
How much VRAM does Llama 2 7B need?
Llama 2 7B requires 4.6 GB of VRAM at Q4_K_M, or 15.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Llama 2 7B?
For Llama 2 7B, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 2 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Llama 2 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 2 7B locally?
Yes — Llama 2 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 2 7B?
At Q4_K_M, Llama 2 7B can reach ~1039 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.62 × 0.65 = ~1126 tok/s
Estimated speed at Q4_K_M (4.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 2 7B?
At Q4_K_M, the download is about 4.20 GB. The full-precision BF16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
Which GPUs can run Llama 2 7B?
52 consumer GPUs can run Llama 2 7B at Q4_K_M. 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 2 7B?
151 devices with unified memory can run Llama 2 7B at Q4_K_M (4.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.