Llama 3.1 Nemotron 70B Instruct HF — Hardware Requirements & GPU Compatibility
Chat
Llama 3.1 Nemotron 70B Instruct is a 70-billion parameter chat model by NVIDIA, created by applying reinforcement learning from human feedback (RLHF) to Meta's Llama 3.1 70B base model. NVIDIA's Nemotron training pipeline focuses on improving helpfulness, accuracy, and response quality beyond the standard Llama instruction tuning. The model requires substantial VRAM for local inference, typically needing multi-GPU setups or high-end professional GPUs. In quantized formats it becomes accessible on workstation-class hardware. It is available in Hugging Face Transformers format and is supported by popular inference engines.
6.5K downloads
2.1K likesSpecifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 70B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-04-13
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Llama 3.1 Nemotron 70B Instruct HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 141.0 GB | 183.3 GB | 140.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama 3.1 Nemotron 70B Instruct HF?
BF16 · 141.0 GBLlama 3.1 Nemotron 70B Instruct HF (BF16) requires 141.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 184+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 183.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.1 Nemotron 70B Instruct HF?
BF16 · 141.0 GB4 devices with unified memory can run Llama 3.1 Nemotron 70B Instruct HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Llama 3.1 Nemotron 70B Instruct HF need?
Llama 3.1 Nemotron 70B Instruct HF requires 141.0 GB of VRAM at BF16. Full 131K context adds up to 42.3 GB (183.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 16 bits ÷ 8 = 140 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
BF16141.0 GBBF16 + full context183.3 GB- Can NVIDIA GeForce RTX 5090 run Llama 3.1 Nemotron 70B Instruct HF?
No — Llama 3.1 Nemotron 70B Instruct HF requires at least 141.0 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Llama 3.1 Nemotron 70B Instruct HF on a Mac?
Llama 3.1 Nemotron 70B Instruct HF requires at least 141.0 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 3.1 Nemotron 70B Instruct HF locally?
Yes — Llama 3.1 Nemotron 70B Instruct HF can run locally on consumer hardware. At BF16 quantization it needs 141.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 Nemotron 70B Instruct HF?
At BF16, Llama 3.1 Nemotron 70B Instruct HF can reach ~21 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 ÷ 141.0 × 0.55 = ~21 tok/s
Estimated speed at BF16 (141.0 GB)
AMD Instinct MI300X~21 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 Nemotron 70B Instruct HF?
At BF16, the download is about 140.00 GB.