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9prodhi/DeepSeek-V4-Flash-moet2pf
DeepSeek-V4-Flash-moet2pf is a machine learning model from 9prodhi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
One file that puts DeepSeek-V4-Flash (159B) on a single NVIDIA DGX Spark. This is the offline-built 2-bit plane store for vLLM-Moet: all 11,008 routed experts (43 MoE layers × 256) of deepseek-ai/DeepSeek-V4-Flash, re…
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Updated Jul 13, 2026
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From the Hugging Face model README
.moet2pf) for vLLM-MoetOne file that puts DeepSeek-V4-Flash (159B) on a single NVIDIA DGX Spark. This is
the offline-built 2-bit plane store for
vLLM-Moet: all 11,008 routed experts
(43 MoE layers × 256) of
deepseek-ai/DeepSeek-V4-Flash,
requantized to sign-symmetric {−4,−1,1,4} 2-bit planes and serialized per-expert-slot
into one O_DIRECT-servable file. The serving stack reads experts straight from this file
into a GPU cache pool — the checkpoint's 148.66 GiB of expert tensors are never
materialized in RAM, which is what makes a 119.7 GiB unified-memory box workable.
| file | ds4-planes.moet2pf — 72.56 GiB (77,913,923,584 bytes) |
| format | MOET2PF1 v1: 4096-aligned header/table/body, per-slot sha256 (contract: tools/plane_file.py) |
| built from | deepseek-ai/DeepSeek-V4-Flash @ 60d8d70770c6776ff598c94bb586a859a38244f1 (pinned in the header) |
| contents | routed-expert FFN weights only (codes13/sc13/codes2/sc2 sections, 6.75 MiB/slot) |
| license | MIT (follows the MIT upstream checkpoint; this file is a requantization of its expert weights) |
This file does not replace the checkpoint — it pairs with it. At boot vLLM still reads the upstream checkpoint for the dense/attention/gate/MTP weights and tokenizer; this file serves only the routed experts. You need both.
16.6 tok/s decode (29.8 with MTP k=2) · prefill ~413–665 tok/s · 64K window · greedy byte-identical 3/3 · needle 8/8 · tool + reasoning parsing on both the OpenAI and Anthropic API surfaces. Full methodology and boot discipline: docs/dgx-spark.md.
# 1. this file + the upstream checkpoint
hf download 9prodhi/DeepSeek-V4-Flash-moet2pf ds4-planes.moet2pf --local-dir /data/models
hf download deepseek-ai/DeepSeek-V4-Flash \
--revision 60d8d70770c6776ff598c94bb586a859a38244f1 \
--local-dir /data/models/DeepSeek-V4-Flash
# 2. serve (image build + all knobs: docs/dgx-spark.md in the GitHub repo)
git clone https://github.com/9prodhi/vLLM-Moet && cd vLLM-Moet
DOCKER_BUILDKIT=1 docker build -f Dockerfile.gb10-v024 -t vllm-moet-gb10:v024 .
docker run -d --name vllm-moet --gpus all --network host --ipc host \
-v /data/models/DeepSeek-V4-Flash:/workspace/models/DeepSeek-V4-Flash:ro \
-v /data/models/ds4-planes.moet2pf:/workspace/models/ds4-planes.moet2pf:ro \
vllm-moet-gb10:v024
Boot only through the image's guarded entrypoint (it preflights the unified-memory
transient and refuses unsafe boots — a raw vllm serve can freeze a Spark).
Every slot carries a sha256; the serving stack re-verifies the file at each boot (~12–15 s O_DIRECT). Offline:
python3 tools/build_plane_file.py verify --dst /data/models/ds4-planes.moet2pf
# -> OK: 11008 slots verified (a single flipped body byte fails loudly, no SIGBUS)
Whole-file checksum:
sha256:998610cd65bbdad730229492dc2e3da2a1723ef73529fb027e676475c70ead35
The file is deterministic output of
tools/build_plane_file.py
over the pinned checkpoint (~7 min on the Spark GPU, peak RSS < 1 GiB; slot bytes are
byte-identical to what the loader would stage in memory — checked by a golden sample at
build time). See docs/dgx-spark.md §3, Option B.