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stringclasses
13 values
gpu
stringclasses
4 values
model
stringclasses
13 values
kernel_family
stringclasses
7 values
kernel_name
stringclasses
328 values
dtype
stringclasses
2 values
held_out
bool
2 classes
M
float64
1
16.4k
N
float64
8
248k
K
float64
2.05k
29.6k
bs
float64
1
1
seq
float64
1
16.4k
n_heads
float64
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64
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float64
64
256
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float64
64
531M
op_type
stringclasses
177 values
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512
launch_grid_size
float64
1
1.04M
dram_bytes_sum
float64
1.27
94.3B
launch_registers_per_thread
float64
16
255
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
void gemmk1_kernel<int, float, 256, 5, 0, 0, 0, 0, cublasGemvTensorStridedBatched<const float>, cublasGemvTensorStridedBatched<const float>, cublasGemvTensorStridedBatched<float>, float, 0>(cublasGemmk1Params<T2, T9, T10, T11, T12, biasType<T11::value_type, T12>::type>)
bf16
true
null
null
null
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128
null
null
null
null
gemv
0.00458
256
8
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
8,192
1
128
null
null
null
null
q_proj
0.13526
256
96
136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
8,192
1
128
null
null
null
null
k_proj
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72
18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
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128
1,024
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null
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128
72
18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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256
96
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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null
null
null
gate
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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28,672
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null
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up
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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null
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
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128
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1
128
null
null
null
null
k_proj
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246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
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1
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null
null
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246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
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136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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112
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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null
null
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up
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
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246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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128
8,192
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o_proj
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
28,672
8,192
1
128
null
null
null
null
gate
0.60003
256
112
475,280,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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128
28,672
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1
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null
null
null
null
up
0.59392
256
112
474,630,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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128
8,192
28,672
1
128
null
null
null
null
down_proj
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256
96
477,190,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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128
8,192
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q_proj
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
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18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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o_proj
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
28,672
8,192
1
128
null
null
null
null
gate
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
28,672
8,192
1
128
null
null
null
null
up
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256
112
475,320,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
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256
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477,130,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
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1
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null
null
null
null
q_proj
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
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1
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18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
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128
72
18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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96
136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
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1
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null
null
null
null
gate
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112
475,870,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
28,672
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
28,672
1
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null
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down_proj
0.38342
256
96
477,130,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
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q_proj
0.13373
256
96
136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
8,192
1
128
null
null
null
null
k_proj
0.02579
128
72
18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
1,024
8,192
1
128
null
null
null
null
v_proj
0.02509
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72
18,900,000
246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
8,192
8,192
1
128
null
null
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o_proj
0.1384
256
96
136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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null
null
null
up
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
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246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
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null
null
null
null
v_proj
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246
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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112
475,630,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
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475,660,000
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_128x128_ldg8_f2f_stages_64x3_tn
bf16
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
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Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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null
null
null
null
o_proj
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
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238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
28,672
8,192
1
128
null
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up
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256
112
475,500,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
true
128
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null
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null
null
down_proj
0.388
256
96
477,130,000
238
Llama-3.1-70B-Instruct_prefill
A100
Llama-70B
gemm
ampere_bf16_s16816gemm_bf16_256x128_ldg8_f2f_stages_64x3_tn
bf16
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128
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null
null
null
null
q_proj
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136,340,000
238
Llama-3.1-70B-Instruct_prefill
A100
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gemm
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End of preview. Expand in Data Studio

AgentPerfBench

LLM inference benchmark: 3,197 main sweep rows and 37 per-layer kernel validation rows, plus 148,077 per-kernel NCU profiles, across 9 models, 14 GPU configurations, and 2 serving engines (vLLM 0.19.0, SGLang 0.5.9). All models served in BF16 except gpt-oss, which uses mxfp4 for projection weights.

Dataset configurations

trace_replay (2,932 rows)

Replays exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). 77 unique (model, hardware, engine) combinations across 17 profiles.

17 profiles: chat-medium, chat-multiturn-long, chat-multiturn-medium, chat-multiturn-short, chat-short, chat-singleturn, coding-singleturn, decode-heavy, osworld-multiturn-long, osworld-multiturn-medium, osworld-multiturn-short, prefill-heavy, random-1k, swebench-multiturn-medium, swebench-multiturn-short, terminalbench-multiturn-medium, terminalbench-multiturn-short

synthetic_distributional (265 rows)

ISL/OSL sampled from lognormal fits to real workload statistics. 38 unique (model, hardware, engine) combinations across 5 profiles.

5 profiles: chat-multiturn-synth, chat-singleturn-synth, osworld-multiturn-synth, swebench-multiturn-synth, terminalbench-multiturn-synth

per_layer_kernel (37 rows)

Per-component operational intensity decomposition and Nsight Compute kernel profiles for Llama-3.1-8B on H100 (prefill phase). Analytical rows provide computed FLOPs, bytes, and OI per model component at batch sizes 1 and 80. NCU rows report measured SM and memory throughput per kernel from an 8-layer forward pass. Record types: analytical_total, analytical_component, ncu_kernel.

kernels_labeled (148,077 rows)

Per-kernel Nsight Compute (ncu) profiles across 4 GPUs (A100, H100, RTX 3090, RTX 2080 Ti) and 13 model/sweep sources.

mse_validation (28 rows)

Curated H100 / Llama-3.1-8B / vLLM validation table for the distributional synthetic replay generator. Paired synthetic and real trace replay runs; supplementary rows preserve no-replacement and high-concurrency debug runs. Raw JSON artifacts referenced through R2 URI columns. Per-run successful/failed request counts retained.

Quality filtering

Configurations where fewer than 75% of requests completed successfully are excluded. Summary metrics are computed from successful requests only.

Config Rows
trace_replay 2,932
synthetic_distributional 265
per_layer_kernel 37
kernels_labeled 148,077
mse_validation 28

Coverage

Hardware

All benchmarks collected on PyTorch 2.10.0.

GPU VRAM HBM bandwidth Peak half-precision TFLOPS
NVIDIA H100 SXM 80 GB 3.35 TB/s 989
NVIDIA A100 SXM4 40 GB 1.56 TB/s 312
NVIDIA RTX 3090 24 GB 936 GB/s 71
NVIDIA RTX 2080 Ti 11 GB 616 GB/s 27

Multi-GPU configurations: 1, 2, 4, or 8 GPUs with tensor parallelism.

Models

All models served in BF16 unless noted.

Model Family Parameters Architecture Notes
Llama-3.1-8B Llama 8B Dense
Llama-3.1-70B Llama 70B Dense
Llama-3.3-70B Llama 70B Dense
Qwen2.5-72B Qwen 72B Dense
Qwen3.5-9B Qwen 9B Dense
Qwen3.5-27B Qwen 27B Dense
Mixtral-8x7B Mixtral 46.7B (12.9B active) MoE
gpt-oss-20b GPT-OSS 21B (3.6B active) MoE mxfp4 projections
gpt-oss-120b GPT-OSS 117B (5.1B active) MoE mxfp4 projections

Engines

  • vLLM 0.19.0
  • SGLang 0.5.9

Schema

Each row in summary.parquet (trace_replay and synthetic_distributional):

Column Type Description
run_id string Deterministic hash of run parameters
model string Model short name
model_family string Model family (llama, qwen, gpt-oss, mixtral)
hardware string GPU configuration (e.g., H100x4)
engine string Serving engine (vllm, sglang)
tensor_parallelism int TP degree
profile string Workload profile name
concurrency int Concurrent request level
num_requests int Total requests in run
duration_s float Total run duration
request_throughput float Requests/second
input_token_throughput float Input tokens/second
output_token_throughput float Output tokens/second
total_token_throughput float Total tokens/second
mean/median/p90/p99_ttft_ms float Time to first token
mean/median/p90/p99_tpot_ms float Time per output token
mean/median/p90/p99_itl_ms float Inter-token latency
mean/median/p90/p99_e2el_ms float End-to-end latency

Loading

from datasets import load_dataset

ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
# or "synthetic_distributional", "per_layer_kernel", "kernels_labeled", "mse_validation"

Benchmark methodology

  • Closed-loop concurrency with semaphore control.
  • 3-request warmup before each configuration.
  • Metrics: TTFT, TPOT, ITL, E2EL, request throughput, token throughput (mean, median, p90, p99).
  • Metrics computed over successful requests only.
  • Collection period: March 2026 onwards.

Limitations

  • Distributional profiles are fitted approximations, not direct production replays.
  • Closed-loop concurrency only; no open-loop (Poisson) arrivals.

Ethical considerations

No PII. Trace-replay profiles derive from open benchmarks (SWE-Bench MIT, TerminalBench, OSWorld). Synthetic profiles use random tokens.

License

Benchmark data released under Apache-2.0. Source datasets retain their original licenses.

Source datasets

Future releases

  • Additional hardware configurations and model families.
  • Open-loop (Poisson) arrival mode.
  • Additional per-kernel roofline profiles.

Citation

@inproceedings{agentperfbench2026,
  title={AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs},
  author={Anonymous},
  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
  year={2026}
}
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