Llama 3.1 Nemotron Nano 8B V1 — Hardware Requirements & GPU Compatibility
Chat
Llama 3.1 Nemotron Nano 8B is an 8-billion parameter chat model by NVIDIA, a compact entry in the Nemotron family derived from Meta's Llama 3.1 architecture. It applies NVIDIA's alignment and fine-tuning techniques to deliver improved response quality over the base Llama 3.1 8B Instruct model at the same parameter count. The model runs on consumer GPUs with 8GB or more of VRAM and supports a 128K token context window. Its small footprint and NVIDIA-tuned quality make it a practical option for local inference on mainstream hardware.
308.6K downloads
219 likesSpecifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-10-15
- License
- Other
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HuggingFace
How Much VRAM Does Llama 3.1 Nemotron Nano 8B V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 33.5 GB | 16.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama 3.1 Nemotron Nano 8B V1?
BF16 · 16.6 GBLlama 3.1 Nemotron Nano 8B V1 (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 33.5 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llama 3.1 Nemotron Nano 8B V1?
BF16 · 16.6 GB21 devices with unified memory can run Llama 3.1 Nemotron Nano 8B V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Llama 3.1 Nemotron Nano 8B V1 need?
Llama 3.1 Nemotron Nano 8B V1 requires 16.6 GB of VRAM at BF16. Full 131K context adds up to 16.9 GB (33.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 16 bits ÷ 8 = 16 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context33.5 GB- Can I run Llama 3.1 Nemotron Nano 8B V1 on a Mac?
Llama 3.1 Nemotron Nano 8B V1 requires at least 16.6 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 Nano 8B V1 locally?
Yes — Llama 3.1 Nemotron Nano 8B V1 can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 Nemotron Nano 8B V1?
At BF16, Llama 3.1 Nemotron Nano 8B V1 can reach ~176 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~40 tok/s. 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 ÷ 16.6 × 0.55 = ~176 tok/s
Estimated speed at BF16 (16.6 GB)
AMD Instinct MI300X~176 tok/sNVIDIA GeForce RTX 4090~40 tok/sNVIDIA H100 SXM~132 tok/sAMD Instinct MI250X~109 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 Nano 8B V1?
At BF16, the download is about 16.00 GB.