Meta Llama 3.1 70B Instruct GGUF — Hardware Requirements & GPU Compatibility
Chat
114.5K downloads
40 likesSpecifications
- Publisher
- MaziyarPanahi
- Family
- Llama 3
- Parameters
- 70B
Get Started
How Much VRAM Does Meta Llama 3.1 70B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 23.1 GB | — | 21.00 GB | Importance-weighted 2-bit, extra small |
| IQ3_XS | 3.30 | 31.8 GB | — | 28.88 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 32.7 GB | — | 29.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 33.7 GB | — | 30.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 37.5 GB | — | 34.13 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 39.5 GB | — | 35.88 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 41.4 GB | — | 37.63 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 43.3 GB | — | 39.38 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 46.2 GB | — | 42.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 52.9 GB | — | 48.13 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 54.9 GB | — | 49.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 63.5 GB | — | 57.75 GB | 6-bit quantization, very good quality |
Which GPUs Can Run Meta Llama 3.1 70B Instruct GGUF?
Q4_K_M · 46.2 GBMeta Llama 3.1 70B Instruct GGUF (Q4_K_M) requires 46.2 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 3.1 70B Instruct GGUF?
Q4_K_M · 46.2 GB11 devices with unified memory can run Meta Llama 3.1 70B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Meta Llama 3.1 70B Instruct GGUF need?
Meta Llama 3.1 70B Instruct GGUF requires 46.2 GB of VRAM at Q4_K_M, or 63.5 GB at Q6_K.
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 4.8 bits ÷ 8 = 42 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M46.2 GB- Can NVIDIA GeForce RTX 4090 run Meta Llama 3.1 70B Instruct GGUF?
Yes, at IQ2_XS (23.1 GB) or lower. Higher quantizations like IQ3_XS (31.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Meta Llama 3.1 70B Instruct GGUF?
For Meta Llama 3.1 70B Instruct GGUF, Q4_K_M (46.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (52.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 23.1 GB.
VRAM requirement by quantization
IQ2_XS23.1 GB~57%Q3_K_S33.7 GB~77%IQ4_XS41.4 GB~87%Q4_K_M ★46.2 GB~89%Q5_K_S52.9 GB~92%Q6_K63.5 GB~95%★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3.1 70B Instruct GGUF on a Mac?
Meta Llama 3.1 70B Instruct GGUF requires at least 23.1 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 3.1 70B Instruct GGUF locally?
Yes — Meta Llama 3.1 70B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 46.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Meta Llama 3.1 70B Instruct GGUF?
At Q4_K_M, Meta Llama 3.1 70B Instruct GGUF 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 ÷ 46.2 × 0.55 = ~63 tok/s
Estimated speed at Q4_K_M (46.2 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 Meta Llama 3.1 70B Instruct GGUF?
At Q4_K_M, the download is about 42.00 GB. The full-precision Q6_K version is 57.75 GB. The smallest option (IQ2_XS) is 21.00 GB.