Meta Llama 3 70B — Hardware Requirements & GPU Compatibility
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Meta Llama 3 70B is a 70.6B-parameter open language model from Meta in the Llama 3 family. At Q4_K_M it needs about 46.57 GB of VRAM — see which GPUs and Macs can run it below.
197.4K downloads
877 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-09-27
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Meta Llama 3 70B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| Q3_K_M | 3.90 | 37.8 GB | — | 34.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 38.8 GB | — | 35.28 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 46.6 GB | — | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 55.3 GB | — | 50.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| 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 Meta Llama 3 70B?
Q4_K_M · 46.6 GBMeta Llama 3 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 Meta Llama 3 70B?
Q4_K_M · 46.6 GB11 devices with unified memory can run Meta Llama 3 70B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Meta Llama 3 70B need?
Meta Llama 3 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 Meta Llama 3 70B?
Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like Q2_K (33.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Meta Llama 3 70B?
For Meta Llama 3 70B, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (48.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 23.3 GB.
VRAM requirement by quantization
IQ2_XS23.3 GBQ4_038.8 GBQ4_K_S43.7 GBQ4_K_M ★46.6 GBQ5_K_S53.4 GBQ8_077.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3 70B on a Mac?
Meta Llama 3 70B requires at least 23.3 GB at IQ2_XS, 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 Meta Llama 3 70B locally?
Yes — Meta Llama 3 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 Meta Llama 3 70B?
At Q4_K_M, Meta Llama 3 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)
~63 tok/s~47 tok/s~39 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meta Llama 3 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_XS) is 21.17 GB.
- Which GPUs can run Meta Llama 3 70B?
No single consumer GPU has enough VRAM to run Meta Llama 3 70B at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Meta Llama 3 70B?
11 devices with unified memory can run Meta Llama 3 70B at Q4_K_M (46.6 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.