We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe. On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse programs together with structured execution feedback on compilation, correctness, and speedup. To make this search reliable, we build backend-specific evaluation services for Triton on NVIDIA GPUs and Maca on MetaX GPUs. On the training side, we convert long-horizon evolution trajectories into step-centric supervision and reinforcement learning signals by retaining correctness-preserving, high-gain revisions, so that the model is optimized as a strong local improver inside the evolutionary loop rather than as a one-shot generator. Under a unified evolutionary protocol, Kernel-Smith-235B-RL achieves state-of-the-art overall performance on KernelBench with Nvidia Triton backend, attaining the best average speedup ratio and outperforming frontier proprietary models including Gemini-3.0-pro and Claude-4.6-opus. We further validate the framework on the MetaX MACA backend, where our Kernel-Smith-MACA-30B surpasses large-scale counterparts such as DeepSeek-V3.2-think and Qwen3-235B-2507-think, highlighting potential for seamless adaptation across heterogeneous platforms. Beyond benchmark results, the same workflow produces upstream contributions to production systems including SGLang and LMDeploy, demonstrating that LLM-driven kernel optimization can transfer from controlled evaluation to practical deployment.
Figures & tables
Figure 1: Comparison of the best program score trajectories across various models over successive evolutionary generations. The program score is formulated as a linear function directly proportional to the speedup, penalizing programs that fail compilation or correctness checks by assigning them appropriately low scores. Throughout the evolution steps, our RL model (Kernel-Smith-235B-RL) exhibited superior efficiency for evolution and peak performance.
Figure 2: An overview of the evolutionary process of our proposed framework and the corresponding model training pipeline.
Models
Level 1 (Easy)
Level 2 (Medium)
Level 3 (Hard)
AVG Level-1/2/3
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Baselines
Qwen3-235B-2507-think
92
0.57
1.86
96
0.86
3.96
84
0.42
0.76
90.67
0.62
2.20
Qwen3.5-397B-think
89
0.55
2.00
90
0.72
3.86
74
0.38
0.61
84.33
0.55
2.16
Minimax-M2.5
70
0.49
2.39
91
0.77
1.06
56
0.30
0.36
72.33
0.52
1.27
Kimi-K2.5
89
0.55
2.00
90
0.72
3.86
74
0.38
0.61
84.33
0.55
2.16
Table 1: Benchmark Results with Baselines and Our Model. Best results are in bold, and second best are underlined.
Models
Activation
Normalization
Reduction&Aggregation
Loss Function
AVG
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Corr
Fast1
Avg AMSR
Baselines
GPT-OSS-20B
100
1.00
3.25
100
0.88
29.7
94.1
0.53
3.63
100
0.80
8.56
97.8
0.78
8.69
Qwen3-30B-A3B
100
1.00
9.03
100
1.00
7.03
100
0.71
17.44
100
0.80
5.67
100
0.87
11.48
Qwen3-235B-2507-think
100
1.00
14.55
100
0.75
35.18
100
0.59
1.36
100
1.00
6.12
100
0.80
12.30
DeepSeek-v3.2-think
100
1.00
10.09
100
0.88
6.06
94.1
0.47
8.60
100
0.60
2.89
97.8
0.73
8.01
Table 2: Evaluation results on MACA kernel generation task.
Figure 3: The acceleration curves during the evolutionary process of applying Kernel-Smith to operators across three real-world scenarios.
Table 3: Isolated kernel benchmarking for the SGLang normal_decode_set_metadata fused Triton kernel, LMDeploy fused MoE routing kernel and DeepSeek engram kernel on NV-H200.
Max batch size
Input length
Output length
Baseline latency (ms)
PR latency (ms)
Rel. gain (%)
16
64
64
251.20
249.87
0.53
16
128
128
430.46
427.80
0.62
16
256
256
794.13
787.26
0.87
16
512
512
1579.02
1569.09
0.63
16
1024
1024
3164.77
3147.41
0.55
16
2048
2048
7014.71
6978.49
0.52
Table 4: End-to-end serving latency reported in the SGLang PR on NV-H200 using sglang.bench_serving with meta-llama/Meta-Llama-3.1-8B-Instruct .
Max batch size
Input length
Output length
Input throughput (tokens/s)
Output throughput (tokens/s)
Baseline
Generated kernel
Rel. gain (%)
Baseline
Generated kernel
Rel. gain (%)
512
1K
4K
471.70
485.86
3.00
1886.78
1943.45
3.00
512
1K
8K
177.62
182.08
2.51
1420.97
1456.67
2.51
512
1K
16K
60.92
62.53
2.64
974.70
1000.45
2.64
512
4K
1K
3885.33
3972.52
2.24
971.33
993.13
2.24
512
8K
1K
3986.93
4067.89
2.03
498.37
508.49
2.03
Table 5: End-to-end DeepSeek-v3.2 inference throughput reported in the LMDeploy release note on NV-H200 with tensor parallelism degree 8.