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# 1k 1k mc1 mc8 mc32 mc128
- benchmark: serving
model: deepseek-ai/DeepSeek-R1-0528 amd/DeepSeek-R1-0528-MXFP4
tp: 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
- benchmark: serving
model: moonshotai/Kimi-K2.6 amd/Kimi-K2.6-MXFP4
tp: 4 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
## Kimi K3 is natively MXFP4 (QAT), so there is no separate amd/ FP4 repo.
## Flags and env mirror the gfx950 single-node TP8 profile from
## https://recipes.vllm.ai/moonshotai/Kimi-K3/hw/mi355x.json
## TP8 only: ~1680 GB minimum footprint fits an 8x MI355X node (2304 GB), not TP4.
- benchmark: serving
model: moonshotai/Kimi-K3
tp: 8
inp: 8192
out: 1024
dtype: auto
# shared K3 sweep across vLLM/SGLang/ATOM; --max-num-seqs is raised to 256 so
# the top of the sweep runs concurrently instead of queueing
max_concurrency: 1 4 8 16 32 64 128 256
env:
VLLM_ROCM_USE_AITER: 1
SAFETENSORS_FAST_GPU: 1
# 1 selects the aiter a8w4 MoE path; 0 falls back to a16w4
AITER_SITUV2_A8W4: 1
AITER_BF16_FP8_MOE_BOUND: 0
VLLM_USE_BREAKABLE_CUDAGRAPH: 0
extra_args:
--moe-backend: auto
--load-format: auto
--gpu-memory-utilization: 0.95
# MoonViT-V2 is only 401M params; TP on it is pure comm overhead
--mm-encoder-tp-mode: data
--max-num-seqs: 256
--max-num-batched-tokens: 4096
# K3 always thinks and returns reasoning_content
--reasoning-parser: kimi_k3
# text-only benchmark; skips loading MoonViT and frees VRAM for KV cache
--language-model-only: true
bench_args:
# gsm8k runs over /v1/completions, where the always-on reasoning is inline and
# exhausts the hardcoded max_gen_toks=2048 budget, so the score is meaningless
--run_accuracy: False
- benchmark: serving
model:
deepseek-ai/DeepSeek-V3.2
amd/DeepSeek-V3.2-MXFP4
zai-org/GLM-5.1-FP8
amd/GLM-5.1-MXFP4
zai-org/GLM-5.2-FP8
amd/GLM-5.2-MXFP4
tp: 4 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
extra_args:
# GLM 5.2 has a context window of 1M tokens, which OOMs with bf16 KV cache
--max-model-len: 101372
--kv-cache-dtype: fp8
arch_overrides:
# MI300 cannot fit FP8 TP4, override to TP8 only
gfx942:
tp: 8
- benchmark: serving
model:
deepseek-ai/DeepSeek-V4-Flash
deepseek-ai/DeepSeek-V4-Pro
tp: 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
VLLM_USE_BREAKABLE_CUDAGRAPH: 0
extra_args:
--kv-cache-dtype: fp8
- benchmark: serving
model:
MiniMaxAI/MiniMax-M3-MXFP8
amd/MiniMax-M3-MXFP4
tp: 4 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
VLLM_USE_BREAKABLE_CUDAGRAPH: 0
VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT: 1
extra_args:
--block-size: 128
--kv-cache-dtype: fp8
--attention-backend: TRITON_ATTN
arch_overrides:
# No native MXFP8 MOE for MI300, can't fit
gfx942:
tp: 8
## gpt-oss w4a8 is gfx950 only
- benchmark: serving
model: openai/gpt-oss-120b amd/gpt-oss120b-w-mxfp4-a-fp8
tp: 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
bench_args:
--lmeval_apply_chat_template: True
## For LLama, Mixtral, etc. on gfx942, use float16 for better GEMM perf
- benchmark: serving
model:
meta-llama/Llama-3.1-405B-Instruct
amd/Llama-3.1-405B-Instruct-FP8-KV
amd/Llama-3.1-405B-Instruct-MXFP4-Preview
meta-llama/Llama-3.3-70B-Instruct
amd/Llama-3.3-70B-Instruct-FP8-KV
amd/Llama-3.3-70B-Instruct-MXFP4-Preview
tp: 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
arch_overrides:
gfx942:
dtype: float16
- benchmark: serving
model:
mistralai/Mixtral-8x22B-Instruct-v0.1
amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
tp: 8
inp: 1024
out: 1024
dtype: auto
max_concurrency: 1 8 32 128
env:
VLLM_ROCM_USE_AITER: 1
arch_overrides:
gfx942:
dtype: float16