oakmindai/DeepSeek-V4.1-Flash-FTW

DeepSeek V4.1 Flash — SparkLab disk-ready checkpoint

This repository is a byte-preserving mirror of deepseek-ai/DeepSeek-V4.1-Flash at revision df42c109f1defefcbfcedbe7d905718a12266e40, published for the SparkLab native NVIDIA DGX Spark research path.

The tensors retain DeepSeek's original mixed checkpoint representation: routed experts are MXFP4, dense projections are MXFP8 with UE8M0 scales, and embeddings and the output head are BF16. No training, tensor conversion, or additional quantization was performed. Although the repository uses the FTW deployment label, DeepSeek V4.1 currently runs through SparkLab's model-owned safetensors reader and packed expert cache rather than the generic FTW container format.

Validated scope

SparkLab currently supports text-only, TP=1, batch-one eager execution with a 2,048-token total context on one 128 GB NVIDIA GB10. The model owns a 64 GiB packed expert LRU and a bounded 24 GiB weight cache, reading the remaining checkpoint from local NVMe.

The default target-only profile measured 0.969 decode tokens/s and 74.387 seconds warm TTFT on a fixed 74-input/128-output greedy probe. Opt-in native greedy DSpark-5 measured 1.053 decode tokens/s and 76.329 seconds warm TTFT, an 8.6% decode improvement. Both DSpark trials reproduced the target-only output hash. These are narrow performance measurements, not general quality, concurrency, long-context, or endurance certification.

The upstream vLLM recipe uses probabilistic draft sampling, block rejection, and adaptive verification. SparkLab's current V4.1 path implements greedy drafting and exact accepted-prefix state commits; probabilistic sampling and adaptive verification remain out of scope.

Download and run

The repository is approximately 480 GiB. Download it to local NVMe:

hf download oakmindai/DeepSeek-V4.1-Flash-FTW \
  --local-dir ~/models/DeepSeek-V4.1-Flash-FTW

Install SparkLab from source, then start the target-only server:

git clone https://github.com/sixteen-miles-labs/sparklab.git
cd sparklab
./install.sh

SPARKLAB_DSV41_EXPERT_CACHE_GB=64 sparklab serve \
  --model ~/models/DeepSeek-V4.1-Flash-FTW \
  --dtype bfloat16 \
  --max-running-requests 1 \
  --max-seq-len-override 2048 \
  --num-tokens 2048 \
  --attention-backend triton \
  --cache-type naive \
  --moe-backend fused \
  --cuda-graph-max-bs 0 \
  --disable-startup-prefill-warmup \
  --port 8000

Enable the measured DSpark profile by adding:

--speculative-method dspark \
--speculative-tokens 5 \
--draft-sample-method greedy

Send a request:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "DeepSeek-V4.1-Flash-FTW",
    "messages": [{"role": "user", "content": "What is 17*19?"}],
    "temperature": 0,
    "max_tokens": 32
  }'

Provenance and license

DeepSeek AI developed and released the architecture, code, tokenizer, and weights. This mirror preserves the upstream MIT license and source files. SparkLab supplies the native runtime, direct packed MXFP4/MXFP8 kernels, bounded disk-backed execution, DSpark integration, validation, and model recipe. Oakmind AI publishes this mirror.

• Upstream model: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
• Exact upstream revision: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/tree/df42c109f1defefcbfcedbe7d905718a12266e40
• SparkLab: https://github.com/sixteen-miles-labs/sparklab
• vLLM architecture recipe: https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4.1-Flash