Llama 7B — Hardware Requirements & GPU Compatibility
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This is a community reupload of Meta's original Llama 1 7B model, published by the huggyllama account on Hugging Face. The original Llama 1 was a 6.7-billion parameter base model released by Meta in early 2023, trained on 1 trillion tokens of publicly available data. It pioneered the wave of open-weight large language models. As a first-generation Llama model, it has been superseded by Llama 2 and Llama 3 in terms of quality and capability. It remains of historical and research interest as the model that catalyzed the open-source LLM ecosystem. This upload provides convenient access in Hugging Face Transformers format.
152.1K downloads
356 likesSpecifications
- Publisher
- huggyllama
- Family
- Llama
- Parameters
- 6.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-07-02
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Llama 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.1 GB | — | 2.86 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.2 GB | — | 2.95 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.6 GB | — | 3.28 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.7 GB | — | 3.37 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.5 GB | — | 4.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.3 GB | — | 4.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.1 GB | — | 5.56 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.4 GB | — | 6.74 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 7B?
Q4_K_M · 4.5 GBLlama 7B (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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llama 7B?
Q4_K_M · 4.5 GB33 devices with unified memory can run Llama 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama 7B need?
Llama 7B requires 4.5 GB of VRAM at Q4_K_M, or 7.4 GB at Q8_0.
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
Q4_K_M4.5 GB- What's the best quantization for Llama 7B?
For Llama 7B, Q4_K_M (4.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.1 GB.
VRAM requirement by quantization
Q2_K3.1 GBQ4_03.7 GBQ4_K_M ★4.5 GBQ5_04.6 GBQ5_K_S5.1 GBQ8_07.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 7B on a Mac?
Llama 7B 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 7B locally?
Yes — Llama 7B 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 7B?
At Q4_K_M, Llama 7B can reach ~655 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 4.5 × 0.55 = ~655 tok/s
Estimated speed at Q4_K_M (4.5 GB)
~655 tok/s~147 tok/s~490 tok/s~405 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 7B?
At Q4_K_M, the download is about 4.04 GB. The full-precision Q8_0 version is 6.74 GB. The smallest option (Q2_K) is 2.86 GB.
- Which GPUs can run Llama 7B?
35 consumer GPUs can run Llama 7B at Q4_K_M (4.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 7B?
33 devices with unified memory can run Llama 7B at Q4_K_M (4.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.