Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF

Qwen3-Next-80B-A3B-Instruct ROCmFP4 STRIX (GGUF) — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

First public ROCmFP4 quant of Qwen3-Next-80B-A3B-Instruct for AMD Ryzen AI Max+ 395 (gfx1151 / Radeon 8060S).

⚠️ Not compatible with upstream llama.cpp. Requires the charlie12345/ROCmFPX fork built with HIP + ROCmFP4 kernels.

Files

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct BF16 source: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF BF16/ 4 shards, 148.51 GiB total (16.01 BPW)

Hardware / stack (validated)

• Ryzen AI Max+ 395, gfx1151, 128 GB unified
• ROCm 7.2.4
• Fork: charlie12345/ROCmFPX @ b41ce12

Build recipe

llama-quantize \
  Qwen3-Next-80B-A3B-Instruct-BF16-00001-of-00004.gguf \
  Qwen3-Next-80B-A3B-Instruct-Q4_0_ROCMFP4_STRIX.gguf \
  Q4_0_ROCMFP4_STRIX

Q4_0_ROCMFP4_STRIX (type 105) is the Strix Halo attention-K/V quality recipe. Dry-run predicted 40641.96 MiB @ 4.28 BPW and the output matched exactly.

Serving

llama-server --host 127.0.0.1 --port 8080 \
  --model Qwen3-Next-80B-A3B-Instruct-Q4_0_ROCMFP4_STRIX.gguf \
  -dev ROCm0 -ngl 999 -fa on --no-mmap \
  --ctx-size 65536 --parallel 1 -b 2048 -ub 1024 -t 16 --poll 50 --jinja

Use the chat endpoint (/v1/chat/completions). Raw /completion with a bare instruction makes this instruct model degenerate into repetition loops.

Measured

Against the UD-Q4_K_XL GGUF of the same model on the same machine, same flags, chat endpoint:

The battery is 24 items: 8 code tasks graded by executing the generated function against assertions, 8 long-tail factual questions, 4 multilingual, 4 maths. Both quants scored 24/24, so quality is at parity and the size and throughput gains come for free.

Throughput caveat: the two arms were measured with different numbers of co-resident models, so treat the speed delta as directionally real but not precisely quantified.

Speculative decoding

Not available. The published MTP head (yomaytk/Qwen3-Next-80B-A3B-Instruct-MTP-HEAD-GGUF) cannot currently be used with llama.cpp:

• It is an MTP-only GGUF, so it will not load via -md (that expects a complete draft model) — it fails with missing tensor 'blk.0.attn_norm.weight'.
• Grafting its 20 blk.48.* tensors into the target (block_count 48 → 49, plus qwen3next.nextn_predict_layers = 1) produces a structurally correct GGUF that still will not load: missing tensor 'blk.48.ssm_conv1d.weight'.

The reason is architectural. Qwen3-Next is a hybrid — every 4th layer is full attention (indices 3, 7, 11 … 47) and the other 36 are Gated DeltaNet / SSM. Layer 48 lands on 48 % 4 == 0, so the loader types it SSM and demands ssm_conv1d, while an MTP layer is attention-shaped. llama.cpp models nextn generally (n_layer_all - n_layer_nextn exists) but does not exempt the nextn layer from this hybrid pattern.

ngram-map-k was also measured and came out at 1.065× — below a 1.15× ship gate.

License

Follow the base model (Qwen/Qwen3-Next-80B-A3B-Instruct) license terms.

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Other public builds of this model

Compiled from Hugging Face repository metadata — file sizes, shipped files, quant variant as named by each repo. No third-party build was run or benchmarked here, so this table makes no speed or quality claim about any of them. It is here so you can see the size and format options at a glance and pick what fits your hardware.

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct. Generated from Hub metadata; download counts move over time.

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Acknowledgements

This build would not exist without the work below. Please star and follow these projects — the quantisation format used here is their engineering, not mine.

ROCmFPX — maintained by charlie12345 / caf The ROCmFP4 / ROCmFPX tensor formats (ggml types 100–106) exist only in this fork. Every ROCmFP4 file in this repository was produced with its llama-quantize, and runs on its runtime. The fork also credits collaborators ciru-ai, Tom Turney, PlunderStruck and Aydan S., and acknowledges AMD for hardware support. Licensed MIT, based on upstream llama.cpp.

llama.cpp — ggml-org and contributors The inference engine, GGUF format and conversion tooling everything here is built on.

AMD ROCm The compute platform these builds target — ROCm 7.2.4 on gfx1151 / Radeon 8060S.

Base model authors — see base_model in the metadata above; all model weights, licences and capabilities are theirs. This repository contributes quantisation and measurement only.

If you use these files, please credit ROCmFPX alongside this repository.

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