DeepSeek-V4-Flash-180B — ROCmFP4 (STRIX_LEAN) GGUF — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

ROCmFP4 quantizations of DeepSeek-V4-Flash-180B, built for AMD Strix Halo (gfx1151) unified-memory systems. Two variants are provided: one retaining the MTP (multi-token prediction) tensors, one without.

These were converted from the MXFP4 release rather than from BF16, so the quantization is MXFP4 → Q4_0_ROCMFP4_STRIX_LEAN.

Files

Each variant is sharded to stay under the 50 GB per-file limit. Point --model at the first shard; llama.cpp loads the rest automatically.

Requirements

ROCmFP4 is not in upstream llama.cpp. You need a build that supports the Q4_0_ROCMFP4_* quantization types (the ROCmFPX fork), compiled for gfx1151:

-DGGML_HIP=ON -DGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DLLAMA_BUILD_WEBUI=OFF

A stock llama.cpp binary will not load these files.

Usage

llama-server \
  --model DeepSeek-V4-Flash-180B-MXFP4-to-ROCmFP4-STRIX_LEAN-00001-of-0000N.gguf \
  --host 127.0.0.1 --port 8080 \
  --n-gpu-layers 999 --flash-attn on -dio \
  --ctx-size 32768

-dio (direct I/O) matters on unified memory — without it the initial load is substantially slower.

⚠️ If the MTP draft command crashes on your build

The separate-model draft-mtp path has known bugs in the legacy charlie12345/ROCmFPX line — reported on Windows 11 / gfx1151 / HIP SDK 7.2 (five stacked bugs, ending in an h-row width mismatch in the draft's embedding buffer).

Fix: build the official repo instead — no patches needed.

git clone https://github.com/ROCmFPX/ROCmFPX.git

The MTP path was reworked there (unified n_embd_out row widths, t_h_nextn reset in llm_graph_result::reset(), ctx_other wired centrally), which covers the whole chain. Reported and verified on that configuration: 24–31 tok/s, coherent output, tool calling working. Legacy-line patch: PR #109.

Linux builds on the legacy line are not known to be affected.

On the MTP variant

The MTP tensors are included so speculative decoding can be evaluated, not because it is recommended by default. On a sibling DeepSeek-V4-Flash ROCmFP4 build, enabling MTP measured a net loss (~0.78×) against the same model with speculation off — the draft acceptance rate did not pay for the extra work.

Measure it on your own hardware and workload before enabling it, and gate on byte-identical greedy output. If you see no gain, the non-MTP variant is the smaller file and the simpler thing to run.

Memory

At ~90 GB these need a machine that can actually place them. On a 128 GB Strix Halo, load them onto an otherwise-idle GPU; large models that follow other large models can fail to fit if the previous allocation has not been released.

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.