DeepSeek-V4.1-Flash-1017 TrellisMX K2 Mix 5GB

A TrellisMX build of deepseek-ai/DeepSeek-V4.1-Flash-1017 (revision 1bae0e1088993b9b82de08f59de5be5a34e42170) for two 96 GB RTX PRO 6000 Blackwell cards at tensor parallel 2. It is the K2 build with 1,131 of its 15,360 routed experts (7.4%) stored at a wider trellis rate: 1,111 at 3 bits and 20 at 4 bits, the rest at 2 bits. The wider experts are the same encodes as the K3 and K4 builds, re-laid out for tensor parallel 2; nothing was re-encoded.

The encodes, the allocation and every measurement on this card ran on an 8x NVIDIA B200 node generously donated by Lambda (@LambdaAPI). Thank you, Lambda.

Built with the SM120 vLLM image by Local Inference Lab and Luke Alonso, the b12x kernels by Luke Alonso with Martin Vit and other contributors, and Local Inference Lab's CSF scale format; TrellisMX was inspired by QTIP and EXL3, and its FP8 trellis decode by Luke Alonso's KQuant/QSRT. Full credits: Credits and CREDITS.md.

Each expert's width was chosen to lower the measured KL divergence most per byte, with Brandon M. Music's ShapleyMCG allocation, under a byte budget sized to keep about 2 million tokens of KV cache on the two cards. The routed experts take 5.08 GB more than the K2 build's (143.79 GB against 138.71 GB). A sibling build with a smaller budget, about 3 million tokens of KV and 2.87 GB more than K2, is DeepSeek-V4.1-Flash-1017-TrellisMX-K2-Mix.

Released under the MIT License, the same license as the base model (LICENSE).

Status: research build; only KLD has been measured, on 64 sequences. The weights are complete; the reference-path KLD below covers 64 of the 512 evaluation sequences, not the full protocol. A test serving image that reads this layout is published as verdictai/deepseek-v41-flash-trellismx:k2mix-fp8-tp2-test-20261020 (sha256:a72e633c8779c6903c4039dc2397e8a55db4a1669f422c9f6516de97e04c0c60). It passes layer-level GPU tests (each width's routes bit-identical to the single-rate kernels, FULL CUDA graph replay with changing routes, the K2 path byte-identical to the published K2 image) but has not been booted as a whole model, so this checkpoint's serving, speed and KV capacity are not measured. The published K2 image and its serve.sh serve the single-rate K2, K3 and K4 layouts only.

What is stored

Per-layer widths are in trellismx/manifest.json (expert_bits, 384 entries per layer). Each trellismx/tp2/layer-LLL-rank-R.safetensors sidecar holds one group of tensors per width present in that layer (k2.*, k3.*, k4.*), each with its ascending global expert_ids, in the same per-expert format as the single-rate builds.

Fidelity

KL(official checkpoint || this build) in nats, over the full vocabulary (129,280 ids), in FP64, at every next-token row (row r predicts token r+1). Teacher: the official checkpoint run in DeepSeek's reference implementation, FP32 logits, published in brandonmusic/DeepSeek-V4.1-Flash-1017-Teacher-Logits (revision 68379d81), folders cuts-2048/ and windows-8192/.

Measured on 64 of the 512 sequences of 2,048 tokens (the 128 evaluation windows cut into 512 fresh sequences; these 64 are cuts 0, 8, 16, ..., 504, which span every domain), 2,047 rows each, 131,008 rows. The full 512-sequence and 8,192-token-window measurement was not run for this build.

On the same 64 sequences and the same path: the K2 build 0.1687 (top-1 87.05%) and the K2 Mix build 0.1466 (87.87%). The intervals overlap because they include the spread between sequences; the builds were scored on the same sequences.

These are the sequences the widths were chosen on, so the figure is not an independent test. For the K2 Mix build the gain over K2 measured 13.1% on these 64 sequences and 8.6% on all 512.

These figures score the routed experts with the official FP8 Engram tables. This checkpoint ships the K2 build's 4-bit Engram tables; on the K2 build that difference measured 0.1439 against 0.1429 on all 512 sequences, so expect this checkpoint to sit about 0.001 nats above the figure.

How it was measured: the base model's layers run in DeepSeek's reference implementation, layer by layer on 4x NVIDIA B200 at model parallel 4, one sequence per forward, with each routed expert reproduced bit for bit at its stored width. The native SM120 kernel path has not been measured.

Files

• config.json, model-000NN-of-00048.safetensors, model.safetensors.index.json, tokenizer files and encoding/: as in the K2 build.
• engram/: the K2 build's 4-bit Engram tables.
• trellismx/manifest.json (schema ds41-trellismx-mix-tp2.v1) and trellismx/tp2/: the routed experts.
• SHA256SUMS: every file's SHA-256.

Teacher logits

The reference logits behind the KLD figures are published in brandonmusic/DeepSeek-V4.1-Flash-1017-Teacher-Logits (revision 68379d81).

Credits

• Compute: Lambda. The allocation and every measurement on this card ran on an 8x NVIDIA B200 node that Lambda donated for this work. Our sincere thanks to Lambda (@LambdaAPI, https://x.com/LambdaAPI) for supporting independent open-model research: this build would not exist without that compute.
• Base model. DeepSeek-V4.1-Flash-1017 by DeepSeek (deepseek-ai/DeepSeek-V4.1-Flash-1017), MIT License (LICENSE).
• Runtime: Local Inference Lab and Luke Alonso. The SM120 runtime this checkpoint targets is built on the vLLM image by Local Inference Lab and Luke Alonso (ghcr.io/local-inference-lab/vllm, https://github.com/local-inference-lab), a fork of vLLM (Apache-2.0, vLLM contributors), and the open-source projects that image bundles, each under its own license: FlashInfer, LMCache, InstantTensor, xgrammar and fastokens.
• Kernels: b12x by Luke Alonso. The MoE, attention and GEMM kernels of that runtime come from b12x (https://github.com/lukealonso/b12x, mirrored at https://github.com/local-inference-lab/b12x) by Luke Alonso, with Martin Vit and other contributors (Apache-2.0).
• CSF encoder: Local Inference Lab. The lossless compressed scale format (CSF) the expert scales are stored in, and its encoder and decoder, are Local Inference Lab's work, shipped in Local Inference Lab's FlashInfer.
• TrellisMX: QTIP and EXL3. TrellisMX, Brandon M. Music's trellis-coded format, was inspired by QTIP (Albert Tseng, Qingyao Sun, David Hou and Christopher De Sa, "QTIP: Quantization with Trellises and Incoherence Processing", NeurIPS 2024) and by ExLlamaV3 and its EXL3 format by turboderp and contributors (https://github.com/turboderp-org/exllamav3, MIT License).
• Codec changes: QSRT and Luke Alonso. Decoding the trellis directly into FP8 (E4M3) tensor-core operands was inspired by Luke Alonso's KQuant/QSRT work and the b12x W4A8 trellis path, which decodes the QSRT trellis to E4M3.
• Allocation: ShapleyMCG by Brandon M. Music. The per-expert widths were chosen with Brandon M. Music's ShapleyMCG allocation: measured KL divergence attributed to groups of experts with Shapley values, a per-expert calibration, and an exact byte knapsack.
• Methods. The per-expert allocation attributes measured KL divergence with Shapley values (Lloyd S. Shapley, "A Value for n-Person Games", 1953) estimated over sampled permutations, and chooses widths with an exact multiple-choice knapsack over byte costs. The encoder's Hessian-based error feedback follows GPTQ (Elias Frantar, Saleh Ashkboos, Torsten Hoefler and Dan Alistarh, ICLR 2023). The incoherence processing and rotations follow QuIP (Jerry Chee, Yaohui Cai, Volodymyr Kuleshov and Christopher De Sa, NeurIPS 2023), QuIP# (Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov and Christopher De Sa, ICML 2024), QuaRot (Saleh Ashkboos et al., NeurIPS 2024) and SpinQuant (Zechun Liu et al., 2024). The UE8M0 block scales follow the OCP Microscaling (MX) Formats Specification (Bita Darvish Rouhani et al., 2023).
• Evaluation data. The evaluation windows behind the KLD figures were built from public datasets, each under the license on its own dataset card: NVIDIA's Nemotron-SFT-Agentic-v2, Nemotron-SFT-Math-v4, Nemotron-SFT-Science-v2 (CC-BY-SA-4.0), Nemotron-SFT-Competitive-Programming-v2, Nemotron-SFT-Instruction-Following-Chat-v3, Nemotron-SFT-Multilingual-v2, Nemotron-SFT-Long-Context-Transduction-v1 (OpenMDW-1.1), Nemotron-SFT-Safety-v2, Nemotron-Pretraining-Legal-v1 (CC-BY-4.0) and Nemotron-Pretraining-Dataset-sample (Nemotron-CC text; NVIDIA Open Data License Agreement); and Nebius's SWE-agent-trajectories (CC-BY-4.0). The windows' sources are listed in the K2 build's evaluation/eval-windows.json.