Llama 2 7B HF — Hardware Requirements & GPU Compatibility
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Llama 2 7B HF is a 6.7B-parameter open language model from Nous Research in the Llama 2 family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 5.42 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 · 4K)Best on 24 GB (Q4_K_M · ~121 tok/s · 4K)Macs from 32 GB (~16 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 2 7B HF
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/Llama-2-7b-hf-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.5 GB free · 4K context
Download: Llama-2-7b-hf.i1-Q4_K_M.gguf4.08 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (5 GB headroom) · Q4_K_M
- Generation speed
- ~43 tok/s
- generation speed
- Cost per 1M output tokens
- $0.49
- per 1M output tokens
How Much VRAM Does Llama 2 7B HF Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~155 tok/s | |
| 4.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~152 tok/s | |
| 4.7 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~141 tok/s | |
| 4.7 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~138 tok/s | |
| 5.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~121 tok/s | |
| 6.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~106 tok/s | |
| 6.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~95 tok/s | |
| 8.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~81 tok/s |
Selected Q4_K_M
4-bit medium quantization6.5GBat 4K context
- Weights
- 4.1 GB
- KV cache
- 2.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.5 GB free · ~121 tok/s
≥50 tok/s needs ≥417 GB/s · RTX 5090 ~215, RTX 3090 Ti ~121
Which Hardware Can Run Llama 2 7B HF?
Q4_K_M · 5.4 GBLlama 2 7B HF (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 6.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 159 devices with unified memory can run Llama 2 7B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.5 GB free · up to 4K ctx
16 GPUs · 1 device family
RTX 3080~91Simulate output on RTX 3080RTX 2080 Ti~74Simulate output on RTX 2080 TiRTX 3070 Ti~73Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.5 GB free · up to 4K ctx
1 GPU
RX 7900 XT~89Simulate output on RX 7900 XT32–40 GB
fits · 25.5 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~215Simulate output on RTX 5090M5 Max36 GB~59Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 2 7B HF
Llama 2 family · 10 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mistral 7B Instruct v0.3Mistral AI | 56.0 | 6.8 GB | ~133 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Chatglm3 6BZ.ai | — | 5.2 GB | ~159 | same size |
Specifications
- Publisher
- Nous Research
- Family
- Llama 2
- Parameters
- 6.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-07-18
Frequently Asked Questions
How much VRAM does Llama 2 7B HF need?
Llama 2 7B HF requires 5.4 GB of VRAM at Q4_K_M, or 14.8 GB at FP16. Full 4K context adds up to 1.1 GB (6.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 4.8 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 1.4 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.
KV Cache + Overhead ≈ 2.5 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Llama 2 7B HF?
For Llama 2 7B HF, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 2 7B HF on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Llama 2 7B HF is Mac Mini M1 (8 GB) at IQ2_XXS; 156 of the 156 Macs we list can run it at some quantization. For Q4_K_M (5.4 GB) you need a Mac with more unified memory.
Can I run Llama 2 7B HF locally?
Yes — Llama 2 7B HF can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 2 7B HF?
At Q4_K_M, Llama 2 7B HF can reach ~886 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~121 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 ÷ 5.42 × 0.65 = ~959 tok/s
Estimated speed at Q4_K_M (5.4 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 HF?
At Q4_K_M, the download is about 4.04 GB. The full-precision FP16 version is 13.48 GB. The smallest option (IQ2_XXS) is 1.85 GB.
Which GPUs can run Llama 2 7B HF?
52 consumer GPUs can run Llama 2 7B HF at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 2 7B HF?
159 devices with unified memory can run Llama 2 7B HF at Q4_K_M (5.4 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.