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2 changes: 2 additions & 0 deletions recipes/Kimi-K2.md
Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,7 @@ This command follows the native ATOM benchmark configuration for `amd/Kimi-K2.7-
export AITER_QUICK_REDUCE_QUANTIZATION=INT4
export AITER_USE_FLYDSL_MOE_SORTING=1
export AITER_AR_1STAGE_MAX_KB=512
export AITER_MXFP4_INTERMEDIATE=1

HSA_NO_SCRATCH_RECLAIM=1 python -m atom.entrypoints.openai_server \
--model amd/Kimi-K2.7-Code-MXFP4 \
Expand Down Expand Up @@ -77,6 +78,7 @@ python -m atom.entrypoints.openai_server \
- Kimi-K2.5 and Kimi-K2.7-Code use a DeepseekV3-style architecture with MLA attention, so they leverage the same optimized kernels (MLA, FP8 KV cache, etc.) as DeepSeek models.
- For the MXFP4 variant, `HSA_NO_SCRATCH_RECLAIM=1` is recommended for stability.
- Non-MoE layers (attention, shared experts, dense MLPs) remain in BF16 for all quantized variants.
- For large concs, `--scheduler-delay-factor 1` is recommended for boost throughputs.

## Performance baseline

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