2025-06-13_amd-advancing-ai-mi350x-and-mi400-ualoe72-mi500-ual256::AMD25-0013
① SA Source
- Source: 開啟完整 SA 文章
- Section:
MI350X and MI355X Specs - Line hint:
77
Context Before
In late 2027, AMD will release the MI500 UAL256 which will feature 256 physical/logical chips and not just 144 physical/logical chips like in VR300 NVL576
MI350X and MI355X Specs
Evidence
However, we expect realized performance to be greater than 10% better for the MI355X because published specs are often never achieved due to power limitations.
Context After
The on-paper specs for the MI350X and the MI355X are both competitive to the HGX B200 for BF16/FP8/FP4 data types (dtypes). We expect that BF16 and FP8 will be used for training while FP8/FP6/FP4 will be used for inference. On the HGX B200, FP6 shares the same physical circuits as FP8, leading to the same FP8/FP6 on paper FLOP/s. On the MI355X, FP6 shares the same physical circuits as FP4, and so FP6 will have the same peak TFLOP/s speed as FP4. This means that MI355X FP6 is 2.2x faster than B200 FP6. In practice, MI355X FP6 will be at least 20% slower than MI355X FP4 due AI chips always being limited by power.
SemiAnalysis benchmarking has shown ↗ that even though the MI300X and the H100 each show the same on-paper TFLOP/s for FP16 as for BF16 (i.e. Nvidia’s FP16 TF = BF16 = 989 TFLOP/s, AMD’s FP16 = BF16 = 1307 TFLOP/s) in practice – each card delivers different realized TFLOPs when running FP16 vs BF16. We will be publishing an article in the near future running microbenchmarks to figure out a realistic TFLOP/s for MI355X FP6 versus FP4.
② Atomic Claim
SemiAnalysis 預期 MI355X realized performance 相對 MI350X 的提升會超過 10%。
- Epistemic Mode:
EXPECTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"comparison_expression": "MI355X >10% better realized performance",
"entities": [
{
"id": "04_knowledge_base/MI355X",
"label": "MI355X"
},
{
"id": "04_knowledge_base/MI350X",
"label": "MI350X"
}
],
"frame_type": "COMPARISON",
"metric": "realized_performance",
"operator": "GREATER_THAN_10_PERCENT",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [
"10%"
],
"temporal_mentions": []
}
}④ Canonical Entity Mapping
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。