Family Nemotron · 30B parameters

Nemotron 3.5 Lightning 30B-A3B (BF16)

Nemotron 3.5 Lightning BF16 checkpoint: MoE 30B / 3B active, 256k context, ~25 GB VRAM at Q4. Fast local reasoning and coding.

🇺🇸 NVIDIA·License NVIDIA Open Model License·Context 256k tokens·Output 2026-08-01·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • MoE 30B / 3B active: high local throughput
  • 256k native context
  • Good for reasoning and coding
  • NVIDIA Open Model License (commercial use OK)
Limitations to know
  • —~17 GB of VRAM in Q4: 16–24 GB GPU recommended
  • —No Ollama tag: install via Hugging Face only
Architecture
Mixture-of-Experts · 30B total parameters · ~3B active per token · 256k context · BF16 weights
Training
Nemotron 3.5 Lightning BF16 checkpoint (NVIDIA). MoE variant optimized for fast inference thanks to its low number of active parameters.
Ideal for
Local reasoningAgents / code256k long context

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$# HuggingFace : nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
25 GB
Q5_K_M
Good quality/size compromise
29 GB
Q8_0
Nearly indistinguishable from FP16
35 GB
FP16
Full precision — server use
66 GB
Fallback CPU · If you don't have a GPU, allow 39 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Nemotron 3.5 Lightning 30B-A3B (BF16)?

To run Nemotron 3.5 Lightning 30B-A3B (BF16) locally with Q4 quantization, you need about 25 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~50t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~85t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~130t/s
RTX 4090, M4 Max, Radeon 7900