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Merge remote-tracking branch 'fork/openai-server' into pd-split-fix
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@ -36,6 +36,11 @@ EPLB expert rearrangement is off by default (`--eplb-interval 0`). Every rearran
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the 32 redundant expert slots (`DEEPINFER_OVERRIDE_NUM_EXTRA_EXPERTS=0`): without rearrangement they are fixed copies
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of experts 0-31, and removing them speeds decode up by 4-5%.
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`--prefill-any-interval` (default 2) trades TTFT for decode speed. A prompt step pauses decode on every DP rank, and
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with 2 almost every arriving request triggers one. That keeps TTFT low; the engine default of 100 adds a ~1.4 s
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floor. At high concurrency, 30-100 gives faster decode and more throughput at the cost of TTFT: on B200 AgentX c256,
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interval 100 gave 255K vs 211K tok/s/GPU and interactivity p50 119 vs 55, with TTFT p50 2.5 vs 0.7 s.
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## Prompt tokenization
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Agentic clients resend the whole conversation every turn, and `tokenizer.encode` needs ~1.7 ms per 1K tokens on a
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@ -67,7 +67,8 @@ def parse_args() -> argparse.Namespace:
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type=int,
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default=2,
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help="DEEPINFER_PREFILL_ANY_ATTEMPT_INTERVAL: a rank with a waiting prompt gets a prefill step at most this "
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"many decode steps later even if other ranks are idle (engine default 100 => ~1.4 s TTFT floor)",
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"many decode steps later even if other ranks are idle (engine default 100 => ~1.4 s TTFT floor). A prompt step "
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"pauses decode on every rank, so at high concurrency a larger value (30-100) trades TTFT for decode speed",
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)
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p.add_argument(
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"--eplb-interval",
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