NVIDIA Nemotron 3 Super 120B A12B Base BF16 — Hardware Requirements & GPU Compatibility
Chat
NVIDIA Nemotron 3 Super 120B A12B Base BF16 is a 123.6B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 74.65 GB of VRAM — see which GPUs and Macs can run it below.
NVIDIA Nemotron 3 Super 120B A12B Base BF16 runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · Q4_K_M · 74.7 GB · ~32 tok/s
Macs from 64 GB (Q2_K · ~36 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run NVIDIA Nemotron 3 Super 120B A12B Base BF16
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (21 GB headroom) · Q4_K_M
- Generation speed
- ~86 tok/s
- generation speed
- Cost per 1M output tokens
- $3.96
- per 1M output tokens
How Much VRAM Does NVIDIA Nemotron 3 Super 120B A12B Base BF16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 53.0 GB | 53.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 53 GB | |
| 60.7 GB | 60.9 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 60.7 GB | |
| 74.7 GB | 74.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 74.7 GB | |
| 88.6 GB | 88.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 88.6 GB | |
| 102.5 GB | 102.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 102.5 GB | |
| 124.1 GB | 124.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 124.1 GB | |
| 247.7 GB | 247.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 247.7 GB |
Selected Q4_K_M
4-bit medium quantization74.7GBat 16K context
- Weights
- 74.2 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 50.7 GB more
Which Hardware Can Run NVIDIA Nemotron 3 Super 120B A12B Base BF16?
Q4_K_M · 74.7 GBNVIDIA Nemotron 3 Super 120B A12B Base BF16 (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 1049K context window can add up to 11.6 GB, bringing total usage to 86.3 GB. No consumer GPU has enough memory. 30 devices with unified memory can run NVIDIA Nemotron 3 Super 120B A12B Base BF16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
64–80 GB
Q4_K_M · best fit Q3_K_M (60.7 GB)
1 device family
Orin~19Simulate output on NVIDIA Jetson AGX Orin 64GB96 GB
fits · 21.4 GB free · up to 1M ctx
2 Mac classes
M3 Ultra*96 GB~32Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~18Simulate output on MacBook Pro 14" M3 Max (96 GB)
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to NVIDIA Nemotron 3 Super 120B A12B Base BF16
Nemotron family · 42 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 122B A10BAlibaba | 109.4 | 75.5 GB | —needs > 24 GB | same size |
| GPT OSS 120BOpenAI | 97.5 | 70.8 GB | —needs > 24 GB | same size |
| Ling 3.0 Flash VLInclusion AI | — | 76 GB | —needs > 24 GB | same size |
| Leanstral 1.5 119B A6BMistral AI | — | 84 GB | —needs > 24 GB | same size |
| Laguna S 2.1poolside | — | 72.5 GB | —needs > 24 GB | same size |
| XORTRON.CriminalComputing.LARGE.2026.3darkc0de | — | 79.8 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 123.6B
- Active parameters
- 13B per token
- Architecture
- NemotronHForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-03-10
- License
- Other
Frequently Asked Questions
How much VRAM does NVIDIA Nemotron 3 Super 120B A12B Base BF16 need?
NVIDIA Nemotron 3 Super 120B A12B Base BF16 requires 74.7 GB of VRAM at Q4_K_M, or 247.7 GB at BF16. Full 1049K context adds up to 11.6 GB (86.3 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 12.1 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron 3 Super 120B A12B Base BF16?
No — NVIDIA Nemotron 3 Super 120B A12B Base BF16 requires at least 53.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for NVIDIA Nemotron 3 Super 120B A12B Base BF16?
For NVIDIA Nemotron 3 Super 120B A12B Base BF16, Q4_K_M (74.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (88.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 53.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run NVIDIA Nemotron 3 Super 120B A12B Base BF16 on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run NVIDIA Nemotron 3 Super 120B A12B Base BF16, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 74.7 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 96GB).
Can I run NVIDIA Nemotron 3 Super 120B A12B Base BF16 locally?
Yes — NVIDIA Nemotron 3 Super 120B A12B Base BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is NVIDIA Nemotron 3 Super 120B A12B Base BF16?
At Q4_K_M, NVIDIA Nemotron 3 Super 120B A12B Base BF16 can reach ~52 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 7.9 GB active ÷ (8000 × 0.65) = 1.51 ms, plus 88 layers × 0.055 ms = 4.84 ms, so 1000 ÷ 6.35 ms = ~157 tok/s
Estimated speed at Q4_K_M (74.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of NVIDIA Nemotron 3 Super 120B A12B Base BF16?
At Q4_K_M, the download is about 74.17 GB. The full-precision BF16 version is 247.22 GB. The smallest option (Q2_K) is 52.53 GB.
Which GPUs can run NVIDIA Nemotron 3 Super 120B A12B Base BF16?
No single consumer GPU has enough VRAM to run NVIDIA Nemotron 3 Super 120B A12B Base BF16 at Q4_K_M (74.7 GB). Multi-GPU or professional hardware is required.
Which devices can run NVIDIA Nemotron 3 Super 120B A12B Base BF16?
30 devices with unified memory can run NVIDIA Nemotron 3 Super 120B A12B Base BF16 at Q4_K_M (74.7 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.