NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning GGUF size and VRAM requirements
unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF is a very large reasoning-focused model with 33.02 billion parameters, built on the nemotron_h_moe architecture. It is released under the other license and has been downloaded 16,465 times.
To run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF locally at a 4,096-token context, its quantized versions need between 19.0 GB (IQ2_M, lowest quality) and 34.74 GB (Q8_K_XL, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q4_K_S, needing about 23.24 GB. That means unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF include IQ2_M, IQ2_XXS, Q2_K_XL, IQ3_S, IQ3_XXS, IQ4_NL, IQ4_NL_XL, IQ4_XS, Q3_K_M, Q3_K_XL, MXFP4, Q4_K_S, Q4_K_M, Q4_K_XL, Q5_K_S, Q5_K_M, Q5_K_XL, Q8_0, Q6_K, Q6_K_XL, Q8_K_XL. The model supports a native context length of up to 1,048,576 tokens; a longer context grows the KV cache and the memory needed.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_M | 4.48 | Good | 17.23 GB | 0.97 GB | 19.0 GB | 2.9 t/s | Offload |
| IQ2_XXS | 4.48 | Good | 17.23 GB | 0.97 GB | 19.0 GB | 2.9 t/s | Offload |
| Q2_K_XL | 4.48 | Good | 17.23 GB | 0.97 GB | 19.0 GB | 2.9 t/s | Offload |
| IQ3_S | 4.56 | Good | 17.53 GB | 0.97 GB | 19.3 GB | 2.9 t/s | Offload |
| IQ3_XXS | 4.72 | Good | 18.12 GB | 0.97 GB | 19.89 GB | 2.8 t/s | Offload |
| IQ4_NL | 4.73 | Good | 18.19 GB | 0.97 GB | 19.97 GB | 2.7 t/s | Offload |
| IQ4_NL_XL | 4.73 | Good | 18.19 GB | 0.97 GB | 19.97 GB | 2.7 t/s | Offload |
| IQ4_XS | 4.73 | Good | 18.19 GB | 0.97 GB | 19.97 GB | 2.7 t/s | Offload |
| Q3_K_M | 4.73 | Good | 18.19 GB | 0.97 GB | 19.97 GB | 2.7 t/s | Offload |
| Q3_K_XL | 4.73 | Good | 18.19 GB | 0.97 GB | 19.97 GB | 2.7 t/s | Offload |
| MXFP4 | 5.27 | Very good | 20.24 GB | 0.97 GB | 22.01 GB | 2.5 t/s | Offload |
| Q4_K_S | 5.58 | Very good | 21.47 GB | 0.97 GB | 23.24 GB | 2.3 t/s | Offload |
| Q4_K_M | 5.79 | Very good | 22.25 GB | 0.97 GB | 24.02 GB | — | Insufficient |
| Q4_K_XL | 5.8 | Very good | 22.28 GB | 0.97 GB | 24.05 GB | — | Insufficient |
| Q5_K_S | 6.01 | Very good | 23.1 GB | 0.97 GB | 24.87 GB | — | Insufficient |
| Q5_K_M | 7.03 | Excellent | 27.0 GB | 0.97 GB | 28.77 GB | — | Insufficient |
| Q5_K_XL | 7.07 | Excellent | 27.19 GB | 0.97 GB | 28.96 GB | — | Insufficient |
| Q8_0 | 8.14 | Excellent | 31.28 GB | 0.97 GB | 33.05 GB | — | Insufficient |
| Q6_K | 8.14 | Excellent | 31.28 GB | 0.97 GB | 33.05 GB | — | Insufficient |
| Q6_K_XL | 8.14 | Excellent | 31.28 GB | 0.97 GB | 33.05 GB | — | Insufficient |
| Q8_K_XL | 8.58 | Excellent | 32.97 GB | 0.97 GB | 34.74 GB | — | Insufficient |
KV cache estimated (architecture unavailable). Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF?
unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF is a reasoning-focused model with 33.02 billion parameters, based on the nemotron_h_moe architecture. It is released under the other license and distributed as GGUF files for local inference.
How much VRAM do you need to run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF?
You need about 23.24 GB of VRAM to run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF entirely on the GPU using the Q4_K_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF on an 8 GB GPU?
Partially. unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q4_K_S), which runs but is slower.
Can I run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF on a 16 GB GPU?
Partially. unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q8_K_XL), which runs but is slower.
Can I run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF fully on the GPU using Q4_K_S (about 23.24 GB).
What context length does unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF support?
unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF supports a native context length of up to 1,048,576 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF?
For unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF, a strong default is Q4_K_M, which needs about 24.02 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.