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.