DeepSeek-V4-Flash-DSpark

Stock DeepSeek-V4-Flash with DSpark speculative decoding. Faster than MTP on 2-4x RTX Pro 6000. By Fraser Price.

The official DeepSeek-V4-Flash (FP8 weights + FP4 experts, unmodified) 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 image 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

Quick start

Needs Docker with the NVIDIA Container Toolkit and 2-4 GPUs (see Hardware).

curl -fsSL https://huggingface.co/fraserprice/DeepSeek-V4-Flash-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:

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.

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 image 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.

These numbers were measured on the abliterated DSpark twin and apply identically here: same architecture, precision, and DSpark module. Abliteration is a rank-3 projection on the base weights that does not change compute.

Latency

Throughput (tok/s)

~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.
• local-inference-lab/rtx6kpro: the sm120 Blackwell / CUDA-13.2 vLLM build (voipmonitor/vllm:eldritch-…) that serves this model, with the dspark speculative method and B12X sparse-MLA stack.
• voipmonitor/vllm: the vLLM image registry the image is published to.

License

MIT, inheriting from the base model.

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Built by Fraser Price, @fraserpricee. Found this useful? A follow is appreciated.