Not another demo or benchmark. @ShoucongChen is a senior member of our technical staff. A real pro...
TL;DR · AI 摘要
Fireworks AI团队使用GLM5.2 Fast模型在4天内完成原本预计1个月的工程任务，速度达400 tokens/秒，成本218美元。
核心要点
- GLM5.2 Fast模型实现400 tokens/秒的处理速度，显著提升工程效率。
- 1个月的项目在4天内完成，成本仅218美元，展示模型经济性。
- Fireworks AI通过实际项目验证模型在复杂软件工程中的应用价值。
结构提纲
按章节快速跳转。
- §项目背景
介绍Fireworks AI团队成员ShoucongChen主导的工程任务。
- ·技术选型
采用GLM5.2 Fast模型实现高效处理。
- ›性能指标
模型运行速度达400 tokens/秒，显著缩短项目周期。
- ·成本分析
4天完成项目仅花费218美元。
- §应用价值
验证大模型在复杂工程中的实际应用潜力。
思维导图
用一张图看清主题之间的关系。
查看大纲文本（无障碍 / 无 JS 友好）
- GLM5.2 Fast模型在工程中的应用
- 项目背景
- ShoucongChen主导的1个月工程任务
- 技术实现
- 400 tokens/秒处理速度
- 4天完成项目
- 成本效益
- 218美元推理成本
金句 / Highlights
值得收藏与分享的关键句。
GLM5.2 Fast runs at ~400 tok/s, finishing before I need to switch tabs.
A ~1 engineer-month task took 4 days and $218 in inference.
Fireworks AI通过实际项目验证模型在复杂软件工程中的应用价值。
A real project, scoped at 1-month. Delivered in 4 days with GLM5.2 Fast.
The best devs deserve >400 t/sec.
Take it for a spin in Claude Code via FireConnect and tell us what you think.
Not another demo or benchmark.
@ShoucongChen is a senior member of our technical staff. A real project, scoped at 1-month. Delivered in 4 days with GLM5.2 Fast. The best devs deserve >400 t/sec. Take it for a spin in Claude Code via FireConnect and tell us what you think.
For complex SWE projects, speed matters more than I thought: slow agents make me context switch. GLM 5.2 Fast runs at ~400 tok/s, finishing before I need to switch tabs. A ~1 engineer-month task took 4 days and $218 in inference. Story: fireworks.ai/blog/glm5p2-fa…
Fireworks AI on X: "Not another demo or benchmark. @ShoucongChen is a senior member of our technical staff. A real project, scoped at 1-month. Delivered in 4 days with GLM5.2 Fast. The best devs deserve >400 t/sec. Take it for a spin in Claude Code via FireConnect and tell us what you think." / X