DSpark Speculative Decoding Acceleration Framework

DSpark is DeepSeek's in-house speculative decoding accelerator, optimized for the V4 MoE architecture.

How It Works

Speculative decoding uses a small draft model to propose candidate token sequences; the main model verifies them in parallel. DSpark adds MoE-specific optimizations:

1. Smart draft selection: Dynamic draft strategy by task type
2. Parallel verification: Tensor-parallel GPU verification of multiple candidates
3. Adaptive fallback: Seamless revert to standard decoding on verification failure

Performance Gains

Scenario	Standard	DSpark	Gain
Code generation	45 tok/s	83 tok/s	+84%
Long-doc summary	38 tok/s	68 tok/s	+79%
Everyday chat	62 tok/s	112 tok/s	+81%

How to Use

DSpark is enabled by default on DeepSeek API — no extra config. Self-hosted users can run dspark enable to activate the module.

API Example (DSpark auto-applied)

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://api.deepseek.com/v1")

# DSpark runs server-side by default — no extra parameters
resp = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Implement an LRU cache in Rust"}],
    stream=True,
)
for chunk in resp:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

3 Acceleration Scenarios

Long-document summary

Generate a structured summary of this 50K-word technical document by section: [document]

Batch code generation

Generate Go handler skeletons for these 10 REST endpoints with unified error handling: [endpoints]

Real-time chat

You are a coding assistant. Reply concisely in English, max 200 words per message.

FAQ

Q: Does DSpark cost extra?

A: No. It's built into V4; billing remains per token with no acceleration surcharge.

Q: How do I enable it self-hosted?

A: Run dspark enable in the DeepSeek inference stack, or set DDEEPSEEK_DSPARK=1.

Q: Which scenarios benefit most?

A: Code generation (+84%), long-doc summary (+79%), and everyday chat (+81%).

See API docs for deployment details.

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📅 Updated July 2, 2026. Benchmarks: DeepSeek internal test env (8× A100 80G, batch size = 1).