Unsloth·NemotronHForCausalLM

NVIDIA Nemotron 3 Super 120B A12B — Hardware Requirements & GPU Compatibility

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159 downloads 2 likes262K context

Specifications

Publisher
Unsloth
Parameters
123.6B
Architecture
NemotronHForCausalLM
Context Length
262,144 tokens
Vocabulary Size
131,072
Release Date
2026-03-11
License
Other

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How Much VRAM Does NVIDIA Nemotron 3 Super 120B A12B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4053.0 GB
Q3_K_S3.5054.6 GB
Q3_K_M3.9060.7 GB
Q4_K_M4.8074.7 GB
Q5_K_M5.7088.6 GB
Q6_K6.60102.5 GB
Q8_08.00124.1 GB

Which GPUs Can Run NVIDIA Nemotron 3 Super 120B A12B?

Q4_K_M · 74.7 GB

NVIDIA Nemotron 3 Super 120B A12B (Q4_K_M) requires 74.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 98+ GB is recommended. Using the full 262K context window can add up to 23.4 GB, bringing total usage to 98.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run NVIDIA Nemotron 3 Super 120B A12B?

Q4_K_M · 74.7 GB

5 devices with unified memory can run NVIDIA Nemotron 3 Super 120B A12B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Related Models

Frequently Asked Questions

How much VRAM does NVIDIA Nemotron 3 Super 120B A12B need?

NVIDIA Nemotron 3 Super 120B A12B requires 74.7 GB of VRAM at Q4_K_M, or 124.1 GB at Q8_0. Full 262K context adds up to 23.4 GB (98.1 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 123.6B × 4.8 bits ÷ 8 = 74.2 GB

KV Cache + Overhead 0.5 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 23.9 GB (at full 262K context)

VRAM usage by quantization

74.7 GB
98.1 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron 3 Super 120B A12B?

No — NVIDIA Nemotron 3 Super 120B A12B requires at least 34.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for NVIDIA Nemotron 3 Super 120B A12B?

For NVIDIA Nemotron 3 Super 120B A12B, Q4_K_M (74.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (85.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 34.5 GB.

VRAM requirement by quantization

IQ2_XXS
34.5 GB
Q2_K
53.0 GB
IQ4_XS
66.9 GB
Q4_K_M
74.7 GB
Q5_K_S
85.5 GB
Q8_0
124.1 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run NVIDIA Nemotron 3 Super 120B A12B on a Mac?

NVIDIA Nemotron 3 Super 120B A12B requires at least 34.5 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 NVIDIA Nemotron 3 Super 120B A12B locally?

Yes — NVIDIA Nemotron 3 Super 120B A12B can run locally on consumer hardware. At Q4_K_M quantization it needs 74.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is NVIDIA Nemotron 3 Super 120B A12B?

At Q4_K_M, NVIDIA Nemotron 3 Super 120B A12B can reach ~39 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 MI300X5300 ÷ 74.7 × 0.55 = ~39 tok/s

Estimated speed at Q4_K_M (74.7 GB)

~39 tok/s
~29 tok/s
~24 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of NVIDIA Nemotron 3 Super 120B A12B?

At Q4_K_M, the download is about 74.17 GB. The full-precision Q8_0 version is 123.61 GB. The smallest option (IQ2_XXS) is 33.99 GB.

Which GPUs can run NVIDIA Nemotron 3 Super 120B A12B?

No single consumer GPU has enough VRAM to run NVIDIA Nemotron 3 Super 120B A12B at Q4_K_M (74.7 GB). Multi-GPU or professional hardware is required.

Which devices can run NVIDIA Nemotron 3 Super 120B A12B?

5 devices with unified memory can run NVIDIA Nemotron 3 Super 120B A12B at Q4_K_M (74.7 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.