IX2-0575
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Migration to Multi Turn Real Multi-Turn Chat and Agentic Coding Datasets - Line hint:
806
Context Before
Although we have made many improvements since our release, there is still much work to be done to achieve the north star goal of providing the most real-world inference benchmarks possible. 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.
Migration to Multi Turn Real Multi-Turn Chat and Agentic Coding Datasets
Evidence
Currently, InferenceX uses completely random tokens as input for benchmarking
Context After
In the near term, we will create a basic multi-turn benchmark with a dataset like allenai/WildChat-4.8M ↗, which captures real users’ multi-turn conversations. In addition to enabling prefix caching on all scenarios, we will enable KV cache CPU offloading, as this is what we see being done in production workloads. This will more accurately evaluate the strengths and weaknesses of each chip. For instance, MI355X has 288GB HBM3e versus B200s 192GB. Therefore, we expect MI355X to perform better in a high concurrency multiturn scenarios as more memory can be allocated to the KV cache. On the other hand, in scenarios where the GPU KV cache is stressed and blocks are offloaded to the CPU, we expect the GBs to excel as these chips have 900GB/s bidirectional CPU-GPU bandwidth, compared to 128GB/s / 256GB/s on HGX with PCIe 5.0 and 6.0, respectively. Moreover, currently we see AMD’s software for CPU offloading is poor, which may negatively affect performance in the same scenarios.
The point is: real-world multiturn datasets test more SOTA inference engine features and can capture more nuanced and robust performance data across all chips.
② Atomic Claim
目前 InferenceX 使用完全隨機的 tokens 作為 benchmark input。
- Epistemic Mode:
ASSERTED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"attribute": "UNSPECIFIED_ATTRIBUTE",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/InferenceX",
"label": "InferenceX"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": [
"目前"
]
},
"value": {
"numeric_mentions": [],
"value_text": "目前 InferenceX 使用完全隨機的 tokens 作為 benchmark input。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | InferenceX | InferenceX |
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。