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
- 2025-03-16
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | 20.9 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 21.0 GB | 3.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.90 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 21.5 GB | 4.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.00 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 Llama 3.1 Nemotron Nano 8B V1?
Q4_K_M · 5.4 GBLlama 3.1 Nemotron Nano 8B V1 (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama 3.1 Nemotron Nano 8B V1?
Q4_K_M · 5.4 GB58 devices with unified memory can run Llama 3.1 Nemotron Nano 8B V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Llama 3.1 Nemotron Nano 8B V1
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Llama 3.1 Nemotron Nano 8B V1 need?
Llama 3.1 Nemotron Nano 8B V1 requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 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
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for Llama 3.1 Nemotron Nano 8B V1?
For Llama 3.1 Nemotron Nano 8B V1, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.8 GB.
VRAM requirement by quantization
IQ2_XXS2.8 GBIQ3_S4.0 GBQ4_04.6 GBQ4_K_M ★5.4 GBQ5_05.6 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 Nemotron Nano 8B V1 on a Mac?
Llama 3.1 Nemotron Nano 8B V1 requires at least 2.8 GB at IQ2_XXS, 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 Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 Nemotron Nano 8B V1?
At Q4_K_M, Llama 3.1 Nemotron Nano 8B V1 can reach ~819 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 tok/s. 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 ÷ 5.4 × 0.65 = ~968 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~968 tok/s~122 tok/s~968 tok/s~819 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 Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 16.00 GB. The smallest option (IQ2_XXS) is 2.20 GB.
- Which GPUs can run Llama 3.1 Nemotron Nano 8B V1?
50 consumer GPUs can run Llama 3.1 Nemotron Nano 8B V1 at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.1 Nemotron Nano 8B V1?
59 devices with unified memory can run Llama 3.1 Nemotron Nano 8B V1 at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.