Llama 2 70B HF — Hardware Requirements & GPU Compatibility
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Llama 2 70B HF is a 69.0B-parameter open language model from Meta in the Llama 2 family. At BF16 it needs about 151.75 GB of VRAM — see which GPUs and Macs can run it below.
15.4K downloads
855 likesSpecifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 69.0B
- Release Date
- 2023-07-11
- License
- Llama 2 Community
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HuggingFace
How Much VRAM Does Llama 2 70B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 151.8 GB | — | 137.95 GB | Brain floating point 16 — preferred for training |
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 Llama 2 70B HF?
BF16 · 151.8 GBLlama 2 70B HF (BF16) requires 151.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 198+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 2 70B HF?
BF16 · 151.8 GB6 devices with unified memory can run Llama 2 70B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llama 2 70B HF need?
Llama 2 70B HF requires 151.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 69.0B × 16 bits ÷ 8 = 138 GB
KV Cache + Overhead ≈ 13.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16151.8 GB- Can NVIDIA GeForce RTX 5090 run Llama 2 70B HF?
No — Llama 2 70B HF requires at least 151.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Llama 2 70B HF on a Mac?
Llama 2 70B HF requires at least 151.8 GB at BF16, 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 2 70B HF locally?
Yes — Llama 2 70B HF can run locally on consumer hardware. At BF16 quantization it needs 151.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 2 70B HF?
At BF16, Llama 2 70B HF can reach ~29 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 ÷ 151.8 × 0.65 = ~34 tok/s
Estimated speed at BF16 (151.8 GB)
~34 tok/s~34 tok/s~29 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 2 70B HF?
At BF16, the download is about 137.95 GB.
- Which GPUs can run Llama 2 70B HF?
No single consumer GPU has enough VRAM to run Llama 2 70B HF at BF16 (151.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 2 70B HF?
6 devices with unified memory can run Llama 2 70B HF at BF16 (151.8 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.