Llama 7B — Hardware Requirements & GPU Compatibility
Chat
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 As a first-generation Llama model, it has been superseded by Llama 2 and Llama 3 in terms of quality and capability.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 |
| Q3_K_L | 4.10 | 3.8 GB | — | 3.45 GB | 3-bit large quantization |
| Q4_1 | 4.50 | 4.2 GB | — | 3.79 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 4.2 GB | — | 3.79 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 4.5 GB | — | 4.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 4.6 GB | — | 4.21 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 5.1 GB | — | 4.63 GB | 5-bit legacy quantization with offset |
| Q5_K_S | 5.50 | 5.1 GB | — | 4.63 GB | 5-bit small quantization |
| 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 GB~75%Q4_03.7 GB~85%Q4_K_M ★4.5 GB~89%Q5_04.6 GB~90%Q5_K_S5.1 GB~92%Q8_07.4 GB~99%★ 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)
AMD Instinct MI300X~655 tok/sNVIDIA GeForce RTX 4090~147 tok/sNVIDIA H100 SXM~490 tok/sAMD Instinct MI250X~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.