Nemotron 3.5 Lightning 30B-A3B

When to pick this model

• Local reasoning or agentic workflows needing fast MoE inference
• Code assistance on single-GPU setups
• Teams wanting NVIDIA's tooling and licensing ecosystem
• Long-context (128K) tasks without frontier-scale hardware

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Nemotron 3.5 Lightning 30B-A3B spills past single consumer GPUs even at Q4_K_M (25 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 raises the footprint to 35 GB, and unquantized FP16 weights take 66 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Nemotron 3.5 Lightning 30B-A3B needs roughly 39 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 50 tokens/sec on entry-level GPUs, on the order of 85 tokens/sec on a mid-range card, and up to 130 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Nemotron 3.5 Lightning 30B-A3B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run Nemotron 3.5 Lightning 30B-A3B?

To run Nemotron 3.5 Lightning 30B-A3B locally at Q4, you need ~25 GB for Q4 weights alone. Hardware option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Strengths

• High throughput thanks to only 3B active parameters
• 128K context window
• Strong at reasoning and code
• ~17GB VRAM at Q4

Limitations

• Full 30B MoE weights still need to fit in memory
• MoE tooling support varies by inference runtime
• Gated weights on Hugging Face

Typical workloads

In our catalog grid, Nemotron 3.5 Lightning 30B-A3B is filed under Local Reasoning, Agents/Code, Fast MoE Inference — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the NVIDIA Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: MoE 30B / 3B active · 128K context

Training: NVIDIA, Nemotron 3.5 line with low activation designed for fast local inference. NVIDIA Open Model License.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

ollama run nemotron-3.5-lightning

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

or all the kits, for life — $49 →

Similar models worth comparing

Frequently asked questions

How much VRAM does Nemotron 3.5 Lightning 30B-A3B need?

At the recommended Q4_K_M quantization, Nemotron 3.5 Lightning 30B-A3B needs about 25 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 66 GB.

Can Nemotron 3.5 Lightning 30B-A3B run without a GPU?

Yes — with roughly 39 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Nemotron 3.5 Lightning 30B-A3B support?

Nemotron 3.5 Lightning 30B-A3B supports a 125k-token context window (128,000 tokens).

Can I use Nemotron 3.5 Lightning 30B-A3B commercially?

Nemotron 3.5 Lightning 30B-A3B ships under the NVIDIA Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Nemotron 3.5 Lightning 30B-A3B on consumer hardware?

Our compatibility engine estimates on the order of 85 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Nemotron 3.5 Lightning 30B-A3B should I download first?

Start with Q4_K_M (25 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.