Meta Llama 3 70B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3 70B Instruct is a 70.6-billion parameter instruction-tuned model from Meta's Llama 3 release. It is fine-tuned for dialogue, coding assistance, and complex reasoning tasks using supervised fine-tuning and RLHF. At the time of release, it was among the most capable openly available models. The model supports an 8K token context window and requires substantial VRAM for local inference, typically needing multi-GPU setups or high-VRAM professional GPUs. It has been widely adopted for local deployment in quantized formats. Released under the Meta Llama 3 Community License.
39.4K downloads
1.5K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-04-17
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Meta Llama 3 70B Instruct 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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Meta Llama 3 70B Instruct?
Q4_K_M · 46.6 GBMeta Llama 3 70B Instruct (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 Instruct?
Q4_K_M · 46.6 GB26 devices with unified memory can run Meta Llama 3 70B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Where to Download Meta Llama 3 70B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Meta Llama 3 70B Instruct need?
Meta Llama 3 70B Instruct requires 46.6 GB of VRAM at Q4_K_M, or 155.2 GB at BF16.
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 Instruct?
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 Instruct?
For Meta Llama 3 70B Instruct, 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_M ★46.6 GBQ5_048.5 GBQ5_K_S53.4 GBBF16155.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3 70B Instruct on a Mac?
Meta Llama 3 70B Instruct 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 Instruct locally?
Yes — Meta Llama 3 70B Instruct 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 Instruct?
At Q4_K_M, Meta Llama 3 70B Instruct can reach ~95 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 46.6 × 0.65 = ~112 tok/s
Estimated speed at Q4_K_M (46.6 GB)
~112 tok/s~112 tok/s~95 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 Instruct?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_XS) is 21.17 GB.
- Which GPUs can run Meta Llama 3 70B Instruct?
No single consumer GPU has enough VRAM to run Meta Llama 3 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Meta Llama 3 70B Instruct?
27 devices with unified memory can run Meta Llama 3 70B Instruct at Q4_K_M (46.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.