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PaoAI/GLM-5.3-Flash-PaoAI-ROCmFP4-STRIX-MTP-GGUF
GLM-5.3-Flash-PaoAI-ROCmFP4-STRIX-MTP-GGUF is a machine learning model from PaoAI. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
The pure-FP4 pool quant of GLM-5.3-Flash for AMD Strix Halo fleets. The whole model on the chip's native 4-bit format (159.2 GiB), designed to be split across two Strix Halo boxes over a direct link when one box isn't…
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.gguf171 GB · 100%
From the Hugging Face model README
The pure-FP4 pool quant of GLM-5.3-Flash for AMD Strix Halo fleets. The whole model on the chip's native 4-bit format (159.2 GiB), designed to be split across two Strix Halo boxes over a direct link when one box isn't enough. The MTP draft head is preserved in the file — but note: on a 2-box pool, speculative decoding is OFF (pool round-trips eat the draft's gains; measured in our fleet).
Brand: PaoAI · community quant, not affiliated with Z.AI.
You have two or more Strix Halo boxes connected directly (USB4/10GbE) and want the full GLM-5.3-Flash in native FP4 split across them. If you have ONE box, use our STRIX-BALANCED instead — a pool of this model loses to a solo smaller quant on both speed and simplicity.
| What | Format |
|---|---|
| Everything (attention, experts, draft head) | Q4_0_ROCMFP4_FAST (native FP4, 4.26 bpw) |
| Router, norms, glue | FP32 (keeper list) |
| MTP draft head (blk.45) | preserved (baked into FP4) |
| Metric | Result |
|---|---|
| File size | 159.2 GiB (single file) |
| 2-box pool decode | ~5.4 t/s (stable ×3, both boxes ~80GB each) |
| Solo on one box | ❌ will wedge the gfx1151 driver (98GB+ allocation wall — see BALANCED/HYBRID for solo options) |
| Quality | not battery-scored (pool-only use case; solo score would not represent pool serving) |
Both binaries need the glm5next arch and the FP4 type. Both live in guevae2/ROCmFPX, branch glm5next, commit 3345156 (glm5next arch + FP4 type merged; head of that branch — also buildable from kingjones30/ROCmFPX main once glm5next is upstreamed). Build with GGML_VULKAN=ON on Strix Halo (gfx1151).
# halo-2 (worker):
ggml-rpc-server --host 0.0.0.0 --port 50052
# BG1 (head):
llama-server -m GLM-5.3-Flash-PaoAI-ROCmFP4-STRIX-MTP.gguf \
--host 0.0.0.0 --port 8081 -ngl 99 -c 32768 --parallel 1 \
--rpc <worker-ip>:50052 -fa on
⚠️ Pool endpoint rule (learned the hard way): use the DIRECT box-to-box link addresses, never routes through a third machine — RPC traffic via a middle box deadlocks after load (head stuck in send, worker in recv).
This is the largest and least convenient of our three models: it needs two boxes, delivers ~5.4 t/s, and can't run solo. We publish it because it completes the recipe set for fleet owners — and because the measurement behind it (pools reward lean bytes-per-token over small file size) is one of the core findings of our work.
MIT, same as the base model. Not affiliated with Z.AI.