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pipenetwork/Nemotron-3-Nano-30B-A3B-context-mlx-4bit
Nemotron-3-Nano-30B-A3B-context-mlx-4bit is a text generation model from pipenetwork. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as other.
The autoregressive context tower of nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16, extracted and converted to MLX (4-bit). This is the frozen base model (NemotronH hybrid: 52 layers = 23 Mamba-2, 6 attention, 23 MoE;
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From the Hugging Face model README
The autoregressive context tower of
nvidia/Nemotron-Labs-TwoTower-30B-A3B-Base-BF16,
extracted and converted to MLX (4-bit). This is the
frozen base model (NemotronH hybrid: 52 layers = 23 Mamba-2, 6 attention, 23 MoE;
128 experts, 6 active + 1 shared; ~30B params, ~3B active). It runs directly in stock
mlx-lm as an ordinary text model — no custom code.
This is the AR backbone only. For the actual two-tower diffusion behavior, use the
Nemotron-Labs-TwoTower-30B-A3B-mlx-*repos below.
📦 Usage guide, examples & benchmarks: https://github.com/PipeNetwork/nemotron-twotower-mlx
pip install mlx-lm
mlx_lm.generate --model pipenetwork/Nemotron-3-Nano-30B-A3B-context-mlx-4bit \
--prompt "The capital of France is" --max-tokens 128
from mlx_lm import load, generate
model, tok = load("pipenetwork/Nemotron-3-Nano-30B-A3B-context-mlx-4bit")
print(generate(model, tok, prompt="The key idea behind Mamba is", max_tokens=128))
The MLX conversion was checked against NVIDIA's reference implementation running on an NVIDIA GB10 (CUDA). Greedy decoding matched token-for-token: 120/120 tokens (100%) and 5/5 top-1 across the test prompts — e.g. both produce "George Washington. He was elected in 1789 and served two terms until 1797." (The AR/context tower is exercised directly; it is the shared backbone the diffusion denoiser also uses.)
Measured on Apple M3 Ultra (512 GB unified memory), MLX 0.31 — steady-state (post-warmup). Peak RAM is the unified-memory high-water mark during generation.
AR / context tower — 128-token single-stream generation:
| Quant | Size | Generation | Peak RAM |
|---|---|---|---|
| 4-bit | 17 GB | 16.1 tok/s | 17.9 GB |
| 6-bit | 24 GB | 13.2 tok/s | 25.7 GB |
| 8-bit | 31 GB | 13.3 tok/s | 33.6 GB |
| bf16 | 59 GB | 13.3 tok/s | 63.2 GB |
TwoTower diffusion — 64 new tokens, block size 16, ≤16 steps/block:
| Quant | Size | Throughput | Denoiser evals | Peak RAM |
|---|---|---|---|---|
| 4-bit | 34 GB | 3.8 tok/s | 64 | 37.1 GB |
| 6-bit | 48 GB | 3.3 tok/s | 64 | 52.5 GB |
| 8-bit | 63 GB | 3.4 tok/s | 64 | 67.9 GB |
| bf16 | 118 GB | 1.5 tok/s | 39 | 136.9 GB |
Diffusion runs steps_per_block denoiser passes per block, so it is slower per token
than the AR tower — lower --steps-per-block trades quality for speed. Higher-precision
builds tend to converge in fewer denoiser evaluations, but each pass moves more memory.
Governed by the NVIDIA Open Model License of the base model.