license: mit
base_model:
- deepseek-ai/DeepSeek-V4-Flash
- fraserprice/DeepSeek-V4-Flash-FP8-Abliterated
- deepseek-ai/DeepSeek-V4-Flash-DSpark
tags: - deepseek
- abliterated
- uncensored
- fp8
- vllm
- blackwell
- rtx-pro-6000
- speculative-decoding
- dspark
DeepSeek-V4-Flash-Abliterated-DSpark
Abliterated DeepSeek-V4-Flash with DSpark speculative decoding. Faster than MTP on 2-4x RTX Pro 6000. By Fraser Price.
The abliterated FP8 DeepSeek-V4-Flash with DeepSeek's DSpark draft module bolted on. DSpark (from DeepSpec) is block-wise speculative decoding: it drafts several tokens per step through a small Markov head, so single-stream decode is ~1.2-1.4x faster than the model's stock MTP at identical quality (speculative decoding is lossless). It runs on a Blackwell / CUDA-13.2 vLLM image that ships the dspark speculative method (voipmonitor/vllm:eldritch-enlightenment-v2226f26-b12x15cd38c-cu132-20260629), the build from local-inference-lab/rtx6kpro.
Update
The serving image has moved from the original fraserpricee/vllm:dspark-cu132-20260627 to the local-inference-lab vLLM build (voipmonitor/vllm:eldritch-enlightenment-v2226f26-b12x15cd38c-cu132-20260629) — a more performant DSpark + sparse-MLA implementation on Blackwell. All credits to them!
The performance numbers below were measured on this V2 build, weights are the same.
Related models
| Repo | What |
|---|---|
| DeepSeek-V4-Flash-FP8-Abliterated | abliterated base, stock MTP (no DSpark) |
| DeepSeek-V4-Flash-Abliterated-DSpark | this repo (abliterated + DSpark) |
| DeepSeek-V4-Flash-DSpark | stock DeepSeek-V4-Flash + DSpark |
Quick start
Needs Docker with the NVIDIA Container Toolkit and 2-4 GPUs (see Hardware).
curl -fsSL https://huggingface.co/fraserprice/DeepSeek-V4-Flash-Abliterated-DSpark/resolve/main/run.sh -o run.sh && bash run.sh
That one command grabs the launcher, downloads the weights (~167 GB) into your Hugging Face cache, pulls the inference image, and serves an OpenAI-compatible API on http://localhost:8000/v1. No other files or arguments needed. On 4 GPUs:
GPUS=0,1,2,3 TP=4 bash run.sh
Everything is overridable by env var:
| Variable | Default | Meaning |
|---|---|---|
GPUS |
0,1 |
GPU indices (comma-separated) |
TP |
2 |
Tensor-parallel size; set to the number of GPUs |
DSPARK_TOKENS |
5 |
DSpark draft tokens per step |
PORT |
8000 |
API port |
MAX_MODEL_LEN |
262144 |
Context length |
GPU_MEM_UTIL |
0.92 |
Fraction of VRAM to use |
HF_REPO |
fraserprice/DeepSeek-V4-Flash-Abliterated-DSpark |
repo to download |
MODEL_DIR |
(HF cache) | serve a local dir instead of downloading |
SERVED_NAME |
DeepSeek-V4-Flash-Abliterated-DSpark |
API model name |
IMAGE |
(pinned DSpark image) | inference container |
Hardware
Tuned for NVIDIA RTX Pro 6000 Blackwell (96 GB, sm_120). Needs roughly 180 GB total VRAM for weights + DSpark module + KV cache at long context, split across GPUs by tensor parallelism.
| Setup | Works? |
|---|---|
2 x RTX Pro 6000 (96 GB), TP=2 |
✅ reference config |
4 x RTX Pro 6000, TP=4 |
✅ more KV headroom / throughput |
| < ~180 GB total VRAM | ❌ won't fit |
The serving image targets Blackwell (sm_120) / CUDA 13.2 and adds the dspark speculative method to vLLM. See local-inference-lab/rtx6kpro for the build source.
Performance
Measured on RTX Pro 6000 Blackwell (PCIe 5.0), FP8 weights + FP8 KV cache, gpu_memory_utilization=0.92, DSpark num_speculative_tokens=5, 256 generated tokens/request. PP is prefill throughput (tok/s); TG is per-request decode throughput (tok/s); total is aggregate throughput (tok/s) across concurrent requests. MTP TG is the base model's shipped single-layer-MTP decode for reference; Decode ↑ is DSpark's gain over it.
Latency
| GPUs | prompt | conc | TTFT p50 | ITL p50 | E2E p50 |
|---|---|---|---|---|---|
| 2 | 1,000 | 1 | 155 ms | 15.6 ms | 1.34 s |
| 2 | 1,000 | 3 | 435 ms | 23.1 ms | 2.04 s |
| 2 | 10,000 | 1 | 1,232 ms | 15.9 ms | 2.32 s |
| 2 | 10,000 | 3 | 2,495 ms | 23.8 ms | 5.30 s |
| 2 | 100,000 | 1 | 14,568 ms | 16.4 ms | 15.71 s |
| 2 | 100,000 | 3 | 29,078 ms | 26.1 ms | 44.77 s |
| 4 | 1,000 | 1 | 127 ms | 11.9 ms | 0.96 s |
| 4 | 1,000 | 3 | 259 ms | 16.3 ms | 1.52 s |
| 4 | 10,000 | 1 | 983 ms | 12.0 ms | 1.87 s |
| 4 | 10,000 | 3 | 2,229 ms | 16.9 ms | 4.06 s |
| 4 | 100,000 | 1 | 12,772 ms | 12.5 ms | 13.59 s |
| 4 | 100,000 | 3 | 26,698 ms | 18.9 ms | 40.11 s |
Throughput (tok/s)
| GPUs | prompt | conc | PP | MTP TG | DSpark TG | Decode ↑ | total |
|---|---|---|---|---|---|---|---|
| 2 | 1,000 | 1 | 8,080 | 190.9 | 224.7 | 1.18× | 1,160 |
| 2 | 1,000 | 3 | 8,377 | 138.4 | 152.5 | 1.10× | 2,120 |
| 2 | 10,000 | 1 | 10,081 | 191.5 | 239.5 | 1.25× | 5,469 |
| 2 | 10,000 | 3 | 10,050 | 90.3 | 106.6 | 1.18× | 7,046 |
| 2 | 100,000 | 1 | 8,525 | 185.4 | 247.1 | 1.33× | 7,965 |
| 2 | 100,000 | 3 | 8,585 | 60.4 | 67.1 | 1.11× | 8,320 |
| 4 | 1,000 | 1 | 9,769 | 233.3 | 322.7 | 1.38× | 1,605 |
| 4 | 1,000 | 3 | 10,578 | 181.9 | 212.8 | 1.17× | 2,927 |
| 4 | 10,000 | 1 | 12,645 | 234.5 | 314.3 | 1.34× | 6,984 |
| 4 | 10,000 | 3 | 12,642 | 115.0 | 148.3 | 1.29× | 9,122 |
| 4 | 100,000 | 1 | 9,734 | 232.8 | 335.8 | 1.44× | 9,194 |
| 4 | 100,000 | 3 | 9,507 | 74.7 | 92.7 | 1.24× | 9,278 |
~1.2-1.4x faster single-stream decode than the base model's shipped MTP, at the same (lossless) quality.
Credits
- deepseek-ai/DeepSeek-V4-Flash: the base model (MIT).
- DeepSeek-AI / DeepSpec and DeepSeek-V4-Flash-DSpark: the DSpark technique and draft weights.
- huihui-ai: the abliteration the refusal directions were derived from (MIT).
- local-inference-lab/rtx6kpro: the Blackwell / CUDA-13.2 vLLM build (
voipmonitor/vllm:eldritch-…) that serves this model, with thedsparkspeculative method and B12X sparse-MLA stack. voipmonitor/vllm: the vLLM image registry the image is published to.
Disclaimer
Uncensored model: the refusal subspace has been removed, so it will not decline requests the way the base model does. Released as-is, for research and lawful use only.
- Not safety-aligned. Outputs may be inaccurate, offensive, or harmful. Apply your own filtering and safeguards.
- No warranty. Provided "as is", without warranty of any kind. You use it entirely at your own risk.
- You are responsible for what you generate. The author does not endorse or accept liability for any unlawful, harmful, or unethical use.
License
MIT, inheriting from the base model and the abliteration source.
Built by Fraser Price, @fraserpricee. Found this useful? A follow is appreciated.