Qwen3.8-27B-MXFP4

Qwen3.8-27B - MXFP4 (imatrix)

4.25-bit MXFP4 quantization of ggml-org/Qwen3.8-27B-GGUF (Qwen3.8-27B-BF16.gguf), produced with imatrix weighting. MXFP4 means e2m1 values with one ue8m0 (e8m0) scale per 32-value block, as in the OCP microscaling spec. 851 tensors, qwen35 architecture.

Provenance

Steps 1-2 are ggml-org's, steps 3-4 are ours, and nothing besides the files above is an input. Step 3 ran over 4894 chunks of wiki.train.raw at n_ctx=512, batch_size=2048, n_seq=4.

Both files in this repo were built by llama.cpp 0.5.0-dev (build 3666, commit 0c666e561) from the fork in timlikesai/llama.cpp#14, which adds the dense MXFP4 file type, the imatrix-weighted scale search and the MXFP4 KV cache path. That revision is what writes general.file_type = 42 (LLAMA_FTYPE_MOSTLY_MXFP4 there); master has since renamed the ftype, so a newer llama-gguf prints a different number for the same file.

Metadata recorded inside 27b-mxfp4-imx.gguf:

• general.architecture = qwen35, general.name = Qwen3.8-27B, general.file_type = 42, general.quantization_version = 2; the base was converted with --no-mtp, so no nextn.* tensors are present
• quantize.imatrix.dataset = wikitext-2-raw/wiki.train.raw, quantize.imatrix.chunks_count = 4894, quantize.imatrix.entries_count = 496
• quantize.imatrix.file - the build-machine path of the imatrix file that is shipped in this repo

Files

• 27b-mxfp4-imx.gguf - MXFP4 weights (4.25 bpw, block-scale e8m0), OCP scale search with imatrix weighting
• 27b.imatrix - the imatrix file from wiki.train.raw used by that scale search, so step 4 is reproducible without re-running step 3

Scale settings: weights use the OCP scale search (target 4.0); the MXFP4 KV cache path uses UOS scaling (e = ceil(log2(amax/7.25))+127, MXAttention) as default.

Checksums

Run it

llama-server -hf timlikesai/Qwen3.8-27B-MXFP4

On consumer Blackwell (sm_120a, RTX 50 series) with timlikesai/llama.cpp#15 these layers default to W4A8: FP8 e4m3 activations with ue8m0 block scales through the kind::mxf8f6f4 block-scaled MMA, which recovers most of the accuracy of q8_1 activations at a fraction of its prefill cost. GGML_CUDA_MMQ_PREC=q4 selects 4-bit activations, q8 selects q8_1.

Measured on 2x RTX 5070 Ti with the file in this repo: wikitext-2 test, n_ctx=4096, 72 chunks, KLD and PPL against the BF16 base of step 2 (llama-perplexity), throughput from llama-bench -p 4096 -n 128 -r 5 (t/s depends only on tensor shapes, so it is the same for any re-quantization of this base). The 4-bit row is what current master does with these weights; the FP8 and q8_1 rows need the #15 build, which is where GGML_CUDA_MMQ_PREC comes from:

Full three-model comparison and the exact repro commands: https://github.com/timlikesai/llama.cpp/pull/15

Related: #14 (MXFP4 file type, imatrix scale search, KV cache) and #15 (CUDA W4A8 for these weights).