* segment sizes marked with an asterisk are estimates pending a measured run
Estimated per-node footprint, weights + KV sharded across the Spark (tensor parallel). Overhead ~8 GB/node; KV at FP16.
one MoE layer · 128 experts, 6+1 fire per token
each expert ≈ 5M params
resident: 52 MoE layers × 128 experts × 5M ≈ 30.6B
active/token: 52 × 6 × 5M ≈ 3.1B experts
+ 947M always-on dense (attention, embeddings, shared stack) = 4.1B active
Eval scores (compare all)
Reported
Recipes (all recipes)
Worked deployments that run this model on DGX Spark — each shows how it fills each node's unified memory.
Nemotron 3 Nano 30B-A3B — NVFP4
NVIDIA's Nemotron 3 Nano 30B-A3B (31.6B hybrid Mamba-MoE, ~3B active) in NVFP4 on one DGX Spark, serving the full 262,144-token context — 61.2 tok/s single-stream, and still 56.0 tok/s on a 131K-token prompt.
Nemotron 3 Nano 30B-A3B — BF16
The unquantized BF16 build of NVIDIA's Nemotron 3 Nano 30B-A3B on one DGX Spark. It serves the same full 262,144-token context as the NVFP4 recipe — but it costs 68 GiB instead of 28.3, and decodes at 28.9 tok/s instead of 61.2. This is the page that tells you what NVFP4 is actually buying.
Optimize
32B params, ~63 GB at bfloat16 — fits on one Spark as-is.
Make it fit
Get the weights (and headroom for KV) inside the 128 GB pool — the hard gate.
Lossless compression
BF16 weights entropy-code ~30% smaller bit-exact — smaller downloads and less bandwidth per token, no quality question at all.
Make it fast
Once it fits: fewer bytes per token and fewer decode steps against 273 GB/s.
Add a speculative decoder
Decode is bandwidth-bound at ~33.6 tok/s ceiling; a trained EAGLE-style draft multiplies tokens per weight-read.
Prune experts for speed
Fewer experts ⇒ smaller working set ⇒ better cache/page behavior, even when it already fits.
Tame the KV cache
At 262144 tokens the KV cache alone is ~14 GB at FP16; quantize or evict.
Make it yours
Change what the model does — adapt, edit, or steer it — independent of size.
Fine-tune it
LoRA/QLoRA adapts behavior on one Spark without touching the base weights.
Edit or steer it
One-shot weight math — no gradients, no corpus.