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anemll/Qwen3.8-2.4T-A95B-FlashMoE-UD-Q1_0
Qwen3.8-2.4T-A95B-FlashMoE-UD-Q1_0 is a text generation model from anemll. Use it when you need the model to write or continue text. It is set up for llama.cpp. The card lists the license as other.
SSD-backed Flash-MoE package for Qwen3.8-2.4T-A95B, exported from the Unsloth UD-Q10 GGUF.
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From the Hugging Face model README
SSD-backed Flash-MoE package for Qwen3.8-2.4T-A95B, exported from the Unsloth UD-Q1_0 GGUF.
This is not a conventional standalone GGUF. The non-routed and shared tensors
are stored in model-dense.gguf; routed expert tensors are stored separately in
a layer-major sidecar/ and loaded into a per-layer resident slot bank on demand.
Use the Flash-MoE-enabled Qwen3.8 branch of
anemll-flash-llama.cpp.
Stock llama.cpp builds that do not understand this package layout or its
IQ1_XXXS routed experts cannot run it.
| Property | Value |
|---|---|
| Architecture | qwen35moe |
| Transformer blocks | 93 |
| Embedding width | 8192 |
| Context metadata | 262,144 tokens |
| Routed experts per layer | 512 |
| Native experts selected per token | 10 |
| Dense/shared GGUF tensors | 1,423 |
| Dense/shared GGUF file size | 36,881,771,680 bytes |
| Routed tensors | 279 |
| Routed sidecar size | 360,374,599,680 bytes |
| Complete upload size | 397,256,650,449 bytes (369.97 GiB) |
The routed sidecar preserves the source GGUF bytes exactly:
IQ1_XXXS (276 tensors).Q2_K (3 tensors).The dense/shared GGUF quant inventory is:
| Quant type | Tensors | Tensor-data bytes |
|---|---|---|
F32 | 838 | 2,171,133,440 |
Q4_K | 2 | 2,288,517,120 |
Q5_K | 420 | 23,391,633,408 |
Q6_K | 162 | 8,876,851,200 |
Q8_0 | 1 | 142,606,336 |
The repository is about 397 GB. Put it on a fast local SSD and preserve the directory layout:
hf download anemll/Qwen3.8-2.4T-A95B-FlashMoE-UD-Q1_0 \
--local-dir ~/Models/Qwen3.8
Expected layout:
~/Models/Qwen3.8/
├── model-dense.gguf
├── flashmoe-package.json
└── sidecar/
├── manifest.json
├── layer_000.bin
├── ...
└── layer_092.bin
Keep the sidecar on fast directly attached storage. This runtime uses SSD reads for cache misses; network filesystems and slow external links will sharply reduce decode speed.
Build the Flash-MoE branch of anemll-flash-llama.cpp, then run from its source
directory:
./build/bin/llama-cli \
-m ~/Models/Qwen3.8/model-dense.gguf \
--moe-mode slot-bank \
--moe-sidecar ~/Models/Qwen3.8/sidecar \
--moe-slot-bank 96 \
--moe-topk 10 \
--moe-cache-io-split 4 \
--moe-prefetch-temporal \
-fit on \
-ub 1 \
-b 1 \
-ngl 999 \
-c 128 \
--no-warmup \
-st \
-p "Hello" \
-n 16
Important runtime choices:
--moe-topk 10: this is the model's native routed width.--slot4 or --slot8; those fused paths do not match native
top-10 routing.-ub 1 and -b 1 select the decode-oriented SSD path.--moe-prefetch-temporal overlaps next-layer reads with current-layer compute.-c only after budgeting KV-cache and runtime memory.anemll-flash-llama.cpp, Qwen3.8
branchThe embedded GGUF metadata reports license: other. Review and comply with the
base model and source repository license terms before use or redistribution.
flashmoe-package.json records the export summary and sidecar/manifest.json
records source offsets, quant types, shapes, exact byte lengths, and repacked
offsets. The package was produced with byte-level sidecar verification enabled.