GLM-5.3-Flash-PaoAI-ROCmFP4-STRIX-HYBRID-GGUF

The maximum-precision hybrid quant of GLM-5.3-Flash for AMD Strix Halo. Finest attention in our lineup (FP4 on the chip's fast format), a near-original draft head (Q8) — built for users who want the best-thinking version and accept a larger file plus one serving accommodation.

Brand: PaoAI · community quant, not affiliated with Z.AI.

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The recipe (what's inside)

| Model part | What it does | Format |

|---|---|---|

| 👁 Attention (attn, indexer) | reads your prompt, tracks context | FP4 (q4_0_rocmfp4_fast, 4.26 bpw) |

| 🧠 Experts (FFN MoE, 132 tensors) | the knowledge | IQ2_XXS (~2.06 bpw, imatrix) |

| ✍️ Draft head (blk.45) | MTP speculative guessing | Q8_0 — near-original |

| 📡 Router, glue, norms | keep signals correct | FP32 (never touched) |

Chain-test results (N=3 medians — Sep 2026)

Same execution-graded coding exam as all PaoAI models: deceptive-code tracing, novel state-machine implementation, thread-safe ring buffer under a 5,000-operation stress harness. Three identical runs per model; medians with spreads.

| Task (what it measures) | Median of 3 | Runs |

|---|---|---|

| Automaton — implement novel rules + exact state trace | 100 | 40 · 100 · 100 |

| Ring buffer — multi-threaded code under stress | 100 | 100 · 100 · 20 |

| Hunt — read lying code: trace it + find the planted bug | 60 | 60 · 60 · 100 |

Task median: 73/100 (runs 67 · 87 · 73 — the steadiest of our builds)

Plain read: reliable on both implementation tasks (automaton 100, threading 100). Finding the specific planted bug-line in deceptive code is its blind spot — it traces the code correctly but names the wrong line.

Speed (decode, tokens/second, per phase)

| Phase | t/s (3-run range) |

|---|---|

| Spec (planning) | 8.8 – 9.0 |

| Hunt (analysis) | 13.1 – 13.8 |

| Automaton (code writing) | 11.7 – 15.1 |

| Ring buffer (code writing) | 8.9 – 10.2 |

| Publish (long output) | 9.3 – 11.3 |

Slowest of our builds — the cost of the CPU-MoE accommodation below.

Facts

| Metric | Result |

|---|---|

| File size | 98.3 GiB (5 shards) |

| Load | ⚠️ requires --n-cpu-moe 8 (see warning) |

| Context | tested at 64K (-c 65536) |

| Served with | MTP speculative decoding (n-max 4), flash attention, KV q8_0 |

Requirements — which llama.cpp build

This model uses two things stock llama.cpp doesn't have:

• the glm5next architecture (GLM-5.3-Flash)
• the Q4_0_ROCMFP4_FAST quant type (type 101 — ROCm FP4, gfx1151-native)

Both live in kingjones30/ROCmFPX, branch main, commit 3345156 or later (the glm5next merge). Tested with GGML_VULKAN=ON on Strix Halo (gfx1151).

If you build from the wrong tree you'll see exactly one of these:

| Build | Error |

|---|---|

| stock llama.cpp | unknown model architecture: 'glm5next' |

| Unsloth's glm5next branch | invalid ggml type 101. should be in [0, 43) |

How to run (measured, locked)

llama-server -m GLM-5.3-Flash-PaoAI-ROCmFP4-STRIX-HYBRID-00001-of-00005.gguf \
  --host 0.0.0.0 --port 8080 -ngl 999 --n-cpu-moe 8 \
  -c 65536 --parallel 1 --spec-type draft-mtp --spec-draft-n-max 4 \
  -fa on --cache-type-k q8_0 --cache-type-v q8_0 --lazy-mode off

⚠️ Driver-wall warning (important on Strix Halo)

This model's ~98GB Vulkan allocation will wedge the gfx1151 driver if loaded full-GPU (unkillable D-state, requires reboot — we proved it three times). The --n-cpu-moe 8 flag above is required, not optional: it keeps the GPU allocation in the proven-safe ~80GB zone by hosting 8 layers' experts in system RAM.

Known limits (honest)

• Needs the CPU-MoE accommodation (above) — slower as a result
• Hunt blind spot: traces deceptive code correctly but misidentifies the planted bug's line number (median 60)
• Long thinking: budget generously for complex tasks

Credits & license

• Z.AI — GLM-5.3-Flash base model (MIT license; included in repo)
• Unsloth — BF16 source + imatrix
• kingjones30 / charlie12345 — ROCmFPX format & recipes
• ggml-org — llama.cpp
• PaoAI — glm5next port, hybrid recipe, fleet benchmarking, chain test

MIT, same as the base model. Not affiliated with Z.AI.