Llama 3.1 70B — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 70B is a 70.6-billion parameter base (pretrained) model from the Llama 3.1 family. It supports a 128K token context window and was trained on a massive multilingual corpus. As a base model, it is designed for fine-tuning and research rather than direct conversational use. The model serves as the foundation for the Llama 3.1 70B Instruct variant and numerous community fine-tunes. It delivers strong performance across language understanding and generation benchmarks. Released under the Llama 3.1 Community License.
76.7K downloads
410 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-09-25
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Llama 3.1 70B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 21.3 GB | — | 19.40 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 23.3 GB | — | 21.17 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 24.3 GB | — | 22.05 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 26.2 GB | — | 23.81 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 30.1 GB | — | 27.34 GB | Importance-weighted 3-bit |
| Q2_K_S | 3.20 | 31.0 GB | — | 28.22 GB | 2-bit small K-quant |
| IQ3_XS | 3.30 | 32.0 GB | — | 29.10 GB | Importance-weighted 3-bit, extra small |
| IQ3_S | 3.40 | 33.0 GB | — | 29.99 GB | Importance-weighted 3-bit, small |
| Q2_K | 3.40 | 33.0 GB | — | 29.99 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 34.0 GB | — | 30.87 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 34.9 GB | — | 31.75 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 37.8 GB | — | 34.39 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 39.8 GB | — | 36.16 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 41.7 GB | — | 37.92 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 43.7 GB | — | 39.69 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 43.7 GB | — | 39.69 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 46.6 GB | — | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 47.5 GB | — | 43.21 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 53.4 GB | — | 48.51 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 55.3 GB | — | 50.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 56.3 GB | — | 51.15 GB | 5-bit large quantization |
| Q6_K | 6.60 | 64.0 GB | — | 58.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 77.6 GB | — | 70.55 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.1 70B?
Q4_K_M · 46.6 GBLlama 3.1 70B (Q4_K_M) requires 46.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 61+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.1 70B?
Q4_K_M · 46.6 GB11 devices with unified memory can run Llama 3.1 70B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (2)
Frequently Asked Questions
- How much VRAM does Llama 3.1 70B need?
Llama 3.1 70B requires 46.6 GB of VRAM at Q4_K_M, or 77.6 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 4.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M46.6 GB- Can NVIDIA GeForce RTX 4090 run Llama 3.1 70B?
Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like IQ2_S (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 3.1 70B?
For Llama 3.1 70B, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (47.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.3 GB.
VRAM requirement by quantization
IQ2_XXS21.3 GB~53%IQ3_XS32.0 GB~73%Q3_K_M37.8 GB~83%Q4_K_M ★46.6 GB~89%Q4_K_L47.5 GB~90%Q8_077.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 70B on a Mac?
Llama 3.1 70B requires at least 21.3 GB at IQ2_XXS, 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 3.1 70B locally?
Yes — Llama 3.1 70B can run locally on consumer hardware. At Q4_K_M quantization it needs 46.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 70B?
At Q4_K_M, Llama 3.1 70B can reach ~63 tok/s on AMD Instinct MI300X. 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 ÷ 46.6 × 0.55 = ~63 tok/s
Estimated speed at Q4_K_M (46.6 GB)
AMD Instinct MI300X~63 tok/sNVIDIA H100 SXM~47 tok/sAMD Instinct MI250X~39 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.1 70B?
At Q4_K_M, the download is about 42.33 GB. The full-precision Q8_0 version is 70.55 GB. The smallest option (IQ2_XXS) is 19.40 GB.