IX2-0574
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Future of InferenceX - Line hint:
802
Context Before
Future of InferenceX
Since the initial release of InferenceX in early October 2025, we have worked hard to continuously improve InferenceX. After release, we spent some time refactoring the codebase to make it more scalable, such that new models and inference techniques can now be added in a “plug and play” fashion. These changes enabled us to seamlessly integrate PD-disagg benchmarks for H100, H200, B200, B300, GB200, GB300, and MI355X. We also added accuracy evaluations to our default benchmark pipeline to ensure visibility into model performance across all configurations.
Evidence
To achieve this goal, we plan to benchmark on real datasets, add an agentic coding performance benchmark, include more SOTA inference optimizations, benchmark more models, and so much more
Context After
Migration to Multi Turn Real Multi-Turn Chat and Agentic Coding Datasets
Currently, InferenceX uses completely random tokens as input for benchmarking. We then vary the ISL/OSL uniformly subject to the distribution [ISL*0.8, ISL], similarly for OSL. Because of the random data, we disable prefix caching in all our benchmarks, as the expected value of a prefix cache hit rate on completely random data is 0%. Furthermore, all the random data is single-turn, meaning each conversation contains only one prompt and one response. While this provides a good baseline Pareto frontier, it is not a practical benchmark setup that mimics real-world production inference workloads.
② Atomic Claim
為達成此目標，團隊計畫使用真實 datasets、加入 agentic coding performance benchmark，並持續加入更多 benchmark 能力與內容。
- Epistemic Mode:
ASSERTED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"attribute": "UNSPECIFIED_ATTRIBUTE",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/Agentic coding",
"label": "agentic coding"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "為達成此目標，團隊計畫使用真實 datasets、加入 agentic coding performance benchmark，並持續加入更多 benchmark 能力與內容。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | agentic coding | 04_knowledge_base/Agentic coding |
⑤ Human Review
請在 Properties 逐項確認：
- 原文 → Atomic Claim 是否忠實
- Atomic Claim → Semantic Frame 是否忠實
- Canonical Entity mapping 是否正確
- Epistemic mode 是否保留原文語氣
- 最後選擇
review_action
Review state
Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。