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Backboard.io's new inference engine outperforms vLLM and SGLang

By PulseAugur Editorial · [1 sources] ·

Backboard.io has developed a new inference engine that significantly outperforms existing solutions like vLLM and SGLang in terms of throughput and latency. Across various models including GLM-5.3, Qwen3.6-35B, and Qwen3.8-27B, and on different NVIDIA GPUs, Backboard's engine demonstrated up to a 4.94x increase in output throughput and a 10x faster median time-to-first-token (TTFT). The engine also excelled in agent-specific tests, handling large context windows and multi-turn interactions with superior performance compared to competitors, even when those competitors used lower-precision models. AI

IMPACT This new inference engine could significantly improve the efficiency and cost-effectiveness of deploying large language models for businesses.

RANK_REASON The item describes a new inference engine developed by a company, detailing its performance against existing tools.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Backboard.io's new inference engine outperforms vLLM and SGLang

How we ranked this

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52 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Tool
The item describes a new inference engine developed by a company, detailing its performance against existing tools.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Jonathan Murray ·

    We built our own inference engine and put it up against vLLM and SGLang on four models and three kinds of GPU.

    <p>357 seconds.</p> <p>That's how long the median request waited for its first token when we ran GLM-5.3 on vLLM under sustained load. Eight B200s. 32 concurrent workflows.</p> <p>Our engine, same model, same box: 3.24 seconds.</p> <p>I didn't build this. Rob and the team did. Th…

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