tanishqkumar

tanishqkumar / ssd

Public

A lightweight inference engine supporting speculative speculative decoding (SSD).

21
1
100% credibility
Found Mar 04, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SSD is a research inference engine that makes large language models generate text up to 2x faster using a novel parallel speculation technique.

How It Works

1
📖 Discover SSD

You find this exciting new way to make AI chatbots generate text twice as fast by reading the research paper.

2
🔧 Get ready

You install a simple tool and prepare your folder of AI models.

3
📁 Point to your models

You tell the system where your AI models and test conversations are stored.

4
📥 Grab test data

You download sample questions to test with.

5
⚡ Run speed tests

You compare how fast different AI engines respond to the same questions and see SSD win big.

6
💬 Start chatting

You have a live conversation with a powerful AI like Llama, watching responses stream in super quickly.

🚀 Enjoy faster AI

Your AI generates text up to twice as fast, perfect for research or fun chats!

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Star Growth

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AI-Generated Review

What is ssd?

SSD delivers a lightweight Python inference engine for LLMs via Speculative Speculative Decoding, where a small draft model speculates multiple token paths in parallel on a separate GPU, verified instantly by the target for up to 2x speedups over autoregressive baselines. Users run tensor-parallel Llama3/Qwen3 inference with PagedAttention, CUDA graphs, and prefix caching on H100s via simple uv sync, env vars for HF cache/datasets, and bench/chat CLI commands benchmarking against SGLang/vLLM. It's exact decoding without quality loss, ideal for lightweight LLM inference.

Why is it gaining traction?

Unlike standard speculative decoding's sequential draft-verify, SSD parallelizes on distinct hardware, slashing overhead on cache hits in its tree-based speculation. Devs hook into drop-in benchmarks across datasets like HumanEval/Alpaca, chat mode with metrics, and easy multi-GPU scaling—beating production engines despite 21 stars. The ICLR 2026 paper and wandb logging make tuning k/fan-out params addictive for perf chasers.

Who should use this?

ML engineers optimizing multi-GPU LLM serving for Llama3 70B or Qwen3 32B. Researchers experimenting with async speculation params on H100 clusters. Teams seeking lightweight PyTorch inference alternatives to vLLM/SGLang without server overhead.

Verdict

Grab it for bleeding-edge benchmarks if you've got GPUs; 1.0% credibility and low stars mean it's raw—solid README but no tests/docs beyond bench scripts. Prod? Wait for polish.

(198 words)

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