⚠️ STOCK llama.cpp WILL NOT LOAD THIS MODEL

Q8_0_ROCMFPX_AGENT is a ROCmFPX quant type — it exists only in charlie12345/ROCmFPX, not upstream llama.cpp.

📦 26.28 GiB, 8.39 bpw · ✅ tools 7/7 both modes 🚀 26.62 tok/s with MTP — 6.2% faster than the plain 8-bit build, because it accepts more draft tokens (0.953 vs 0.911). ⚠️ Without MTP the two are indistinguishable.

Qwen3.8-27B — ROCmFPX 8-bit AGENT routing (Q8_0_ROCMFPX_AGENT) GGUF

The agent / tool-call coherent 8-bit ROCmFPX routing of Qwen3.8-27B for AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo). Quantized from the 51.3 GiB BF16 GGUF.

⚖️ Why this variant exists — and when it does NOT

The routing difference is structural. Across the 17-tensor *output.weight group:

AGENT keeps 12 more attention-output projections at full Q8_0 — that is what the extra 0.35 GiB buys.

With MTP, that buys real speed

Ryzen AI MAX+ 395 (gfx1151, ROCm 7.2.4), median of 3, warm-up discarded, idle box, shipped flags (--spec-type draft-mtp, Q4_0 draft head, --spec-draft-n-max 4):

+6.2% over plain, ranges disjoint. The mechanism is visible in the acceptance rate: keeping more attention-output precision makes the MTP draft head's guesses land more often, so more speculated tokens survive verification.

⚠️ Without MTP there is no measurable difference

Run standalone, the two builds are 7.92 vs 7.82 tok/s (this one marginally slower), and both score 7/7 tool calling in thinking and non-thinking — identical across multi-arg, nested-object, enum, declines, multi-turn, streaming and parallel calls. Our 7-case suite cannot separate them.

⇒ Take this variant if you serve with MTP. Take the plain build if you do not. And if you want raw throughput, the 4-bit build beats both by ~1.44×.

llama-server -m Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf \
  --spec-type draft-mtp --model-draft mtp-Qwen3.8-27B-Q4_0.gguf \
  --spec-draft-ngl 99 --spec-draft-n-max 4 \
  -ngl 999 -fa on -fit off --jinja --ctx-size 32768

Verified

17×23 ⇒ ✅ 391 · capital of Japan ⇒ ✅ Tokyo · days in 2024 ⇒ ✅ 366 · tools 7/7 thinking, 7/7 non-thinking.

token_embd.weight Q8_0, 851 tensors, arch=qwen35.

⚠️ 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.

🩹 Prompt caching with the MTP draft head — fixed

Reported by a user of this repo: with --spec-type draft-mtp loaded, llama-server disabled prefix caching entirely. Every agentic turn reprocessed the whole prompt. Reproduced here on an 8045-token stable prefix:

279× less prompt processing per turn, with MTP still drafting.

Root cause

The saved speculative state is the MTP boundary — the target model's pre-norm hidden row at the cached prompt's exact end position. Any partial-prefix reuse would leave it describing a position that no longer exists, so the server demanded an exact full-prefix match and otherwise reprocessed cold, erasing its own context checkpoints on the way.

The fix

patches/mtp-prompt-cache-fix.patch (4 files, applies to 2809dc5) captures the speculative boundary inside the context checkpoint (common_prompt_checkpoint::data_spec). create_checkpoint runs between decode batches — exactly where that boundary is valid — so exact state is saved and restored together with the KV, never rebuilt.

⛔ Two approaches were tried first and rejected: rebuilding the boundary from a zero-fill changed the model's output (deterministically, 3/3), and truncating the KV back to the reuse point is impossible here — the bounded rollback window is 4 tokens against the 333 a real turn needs. Exact state restore is the only shape that preserves output.

Verification

Independently gated 10/10: same prompt cold vs warm, temperature 0, byte-identical every run, with the cache genuinely engaged (cache_n=5101, not a vacuous pass). The output hash also matches the unpatched build, so behaviour is unchanged. Fails closed — an unreachable rollback logs reason=spec-checkpoint-missing and cold-reprocesses rather than guessing.

Related upstream

This is the same family as open llama.cpp issues #20225, #19794 and #24055 — checkpoints being invalidated on hybrid/recurrent models. This patch is not upstreamed; it is offered here as-is.

What was NOT measured

• No perplexity, no quality A/B vs BF16 or the plain 8-bit build. We have shown a structural difference in tensor types and no measurable behavioural difference.
• No long-context testing (131,072 supported), no coding/reasoning benchmark.
• Our tool suite is 7 cases. A 7/7 tie is not proof of equivalence — it is the limit of what this instrument can resolve. The MTP acceptance rate did separate the builds where the tool suite could not.
• An earlier revision of this card said no advantage over the plain build could be measured. That was true without MTP and wrong with it; the table above supersedes it.

Base model licence inherited. Credit for the model goes to Qwen.