* 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 · 256 experts, 8+1 fire per token
each expert ≈ 7M params
resident: 26 MoE layers × 256 experts × 7M ≈ 47.1B
active/token: 26 × 8 × 7M ≈ 1.5B experts
+ 2.0B always-on dense (attention, embeddings, shared stack) = 3.5B active
Recipes (all recipes)
Worked deployments that run this model on DGX Spark — each shows how it fills each node's unified memory.
Optimize
49B params, ~98 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 ~39.2 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 1048576 tokens the KV cache alone is ~8 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.