You can deploy this model and start earning money today!

YanissAmz/
Nemotron-3-Puzzle-75B-A9B-GGUF

Name: Nemotron-3-Puzzle-75B-A9B-GGUF
Availability: InStock

Views

No views yet

Nemotron-3-Puzzle-75B-A9B — GGUF

First GGUF release of NVIDIA's Nemotron-3-Puzzle-75B-A9B (hybrid mamba2/attention/latent-MoE, 75B total / 9B active, 262k context, MTP draft head).

Converted from the official FP8 checkpoint (weight scales absorbed at conversion — no double quantization), then quantized from the Q8_0 master with an importance matrix.

Files

file	size	note
Puzzle-75B-A9B-Q8_0-0000X-of-00002.gguf	77.7 GiB (2 shards)	master, near-lossless — point llama.cpp at shard 00001, the rest loads automatically
Puzzle-75B-A9B-Q4_K_M-0000X-of-00002.gguf	48.1 GiB (2 shards)	reference k-quant, fastest decode
Puzzle-75B-A9B-NVFP4.gguf	45.0 GiB	experts NVFP4, everything else Q8_0
Puzzle-75B-A9B-UD-IQ4-XL.gguf	41.6 GiB	experts IQ4_XS; attn Q8_0, ssm/shexp Q6_K, ffn_latent Q8_0
puzzle-imatrix.gguf	0.2 GiB	reusable imatrix (calibration_datav3)

Requirements

Not yet supported by mainline llama.cpp — needs per-layer heterogeneous MoE arrays and the 2-sub-block MTP head. Use the puzzle-port branch until the PR is merged: [PR_LINK]

Measured (Strix Halo 128GB unified, Radeon 8060S, `-ngl 99`; PPL = wikitext-2 test, 24 chunks)

quant	PPL	decode t/s	prefill t/s	backend
Q8_0	5.325	10.2	189	Vulkan
Q4_K_M	5.404	19.9	238	ROCm
UD-IQ4-XL	5.377	17.7	211	ROCm
NVFP4	5.383	16.6	243	ROCm

All three 4-bit variants sit within noise of each other on PPL (±0.08); pick by speed/size trade-off.

⚠️ On Strix Halo (gfx1151) use the ROCm/HIP backend for the 4-bit quants: Vulkan decode collapses to ~2.7 t/s on this model's MoE (mul_mat_id slow path). Q8_0 exceeds the ROCm allocation limit → run it on Vulkan.

MTP speculative decoding (--spec-type draft-mtp) loads and drafts correctly, but is currently slower than plain decoding (~13 vs 16.6 t/s): llama.cpp cannot yet roll back mamba2 recurrent states, which throttles draft attempts. Leave it off for now.

Notes

Reasoning model: llama-server parses the thinking channel natively.
AI-assisted work; everything reviewed and validated end-to-end on my hardware.

YanissAmz/Nemotron-3-Puzzle-75B-A9B-GGUF