What GPU do I need to run nvidia/Llama-3.1-Nemotron-Nano-8B-v1?
A 8B language model for chat and instruction-following. 8.0B parameters, published in BF16. View on Hugging Face
Llama-3.1-Nemotron-Nano-8B-v1 is published by nvidia on Hugging Face, with 218,118 downloads and 224 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
What Llama-3.1-Nemotron-Nano-8B-v1 is
Llama-3.1-Nemotron-Nano-8B-v1 is a 8B-parameter language model published by NVIDIA on Hugging Face. It reads images alongside text. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from Llama-3.1-Nemotron-Nano-8B-v1's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 15.0 GB | 17.9 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 7.5 GB | 9.0 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 3.7 GB | 4.5 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Llama-3.1-Nemotron-Nano-8B-v1 at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3.1-Nemotron-Nano-8B-v1: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 131,072 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.50 GB |
| 32K | 4.0 GB |
| 128K | 16.0 GB |
Computed from the layer, head and window counts in the model's own configuration (nvidia/Llama-3.1-Nemotron-Nano-8B-v1), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Llama-3.1-Nemotron-Nano-8B-v1: common questions
Does Llama-3.1-Nemotron-Nano-8B-v1 fit on a 24 GB GPU?
Yes. At BF16 it needs 17.9 GB of VRAM, so a 24 GB card holds it with 6.1 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM Llama-3.1-Nemotron-Nano-8B-v1 can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 17.9 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Llama-3.1-Nemotron-Nano-8B-v1 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
How to run Llama-3.1-Nemotron-Nano-8B-v1
Run Llama-3.1-Nemotron-Nano-8B-v1 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve nvidia/Llama-3.1-Nemotron-Nano-8B-v1 --tensor-parallel-size 1Run Llama-3.1-Nemotron-Nano-8B-v1 with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Llama-3.1-Nemotron-Nano-8B-v1-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Llama-3.1-Nemotron-Nano-8B-v1-GGUFSource: https://huggingface.co/unsloth/Llama-3.1-Nemotron-Nano-8B-v1-GGUF
Deploy Llama-3.1-Nemotron-Nano-8B-v1 on Aquanode
Aquanode has no one-click deploy template for Llama-3.1-Nemotron-Nano-8B-v1; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX A5000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Llama-3.1-8B (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct-FP8 (8.0B, F8_E4M3)
Alternatives at this size
Other models for text-generation within about a third of Llama-3.1-Nemotron-Nano-8B-v1's 8.0B parameters, from other model lines.
- Qwen3-8B (8.2B, BF16)
- Mistral-7B-Instruct-v0.2 (7.2B, BF16)
- granite-4.1-8b (8.8B, BF16)
- DeepSeek-R1-0528-Qwen3-8B (8.2B, BF16)
- gemma-2-9b-it (9.2B, BF16)
More on Llama-3.1-Nemotron-Nano-8B-v1
Fits on a 24 GB GPU at BF16: every model that fits in 24 GB.
Fits on a 12 GB GPU at FP8: every model that fits in 12 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Related reading: RTX A5000 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, Best GPU for LLM inference, and What AI model fine-tuning is.
