Yes. For your frontier-AI goal, I would make the roadmap specifically about GPU kernel programming for AI, not generic CUDA.
🚀 GPU Kernel Programming Roadmap for Frontier AI
1 GPU KERNEL PROGRAMMING 2 │ 3 ┌───────────────┴───────────────┐ 4 │ │ 5 CUDA Fundamentals GPU Architecture 6 │ │ 7 └───────────────┬───────────────┘ 8 ↓ 9 Memory Optimization 10 ↓ 11 Parallel Algorithms 12 ↓ 13 Kernel Optimization 14 ↓ 15 CUDA Profiling 16 ↓ 17 Triton 18 ↓ 19 Deep Learning Kernels 20 ↓ 21 Transformer Kernels 22 ↓ 23 Attention / FlashAttention 24 ↓ 25 Tensor Core Programming 26 ↓ 27 Quantized Kernels 28 ↓ 29 LLM Inference Optimization 30 ↓ 31 Advanced GPU Systems
Phase 1 — CUDA Fundamentals
1. CUDA architecture
Learn:
- GPU vs CPU
- SM — Streaming Multiprocessor
- CUDA cores
- Tensor Cores
- GPU memory
- L1/L2 cache
- registers
- shared memory
- global memory
Understand:
1GPU 2 ├── SM 3 │ ├── Warp 4 │ │ └── Threads 5 │ ├── Registers 6 │ └── Shared Memory 7 │ 8 └── Global Memory
2. CUDA kernel
Learn:
1__global__ 2__device__ 3__host__
Most important initially:
1[object Object],{ 2 ... 3}
Learn how to launch:
1kernel<<<blocks, threads>>>();
3. Thread indexing
Master:
1threadIdx 2blockIdx 3blockDim 4gridDim
Most important formula:
1[object Object], i = 2 blockIdx.x * blockDim.x 3 + threadIdx.x;
Also learn 2D/3D indexing:
1[object Object], row = 2 blockIdx.y * blockDim.y 3 + threadIdx.y; 4 5,[object Object], col = 6 blockIdx.x * blockDim.x 7 + threadIdx.x;
Phase 2 — GPU Parallelism
4. Threads
Learn:
1Thread 2 ↓ 3Block 4 ↓ 5Grid
Understand how thousands/millions of threads execute.
5. Warps
Master:
11 warp = 32 threads
Learn:
- warp execution
- SIMT
- warp scheduling
- warp divergence
- warp-level operations
Important functions:
1__shfl_sync() 2__ballot_sync()
Don't worry about memorizing every warp primitive initially.
Phase 3 — GPU Memory
This is one of the most important sections.
6. Memory hierarchy
Master:
1Registers 2 ↓ 3Shared Memory 4 ↓ 5L1 Cache 6 ↓ 7L2 Cache 8 ↓ 9Global Memory
Understand:
- latency
- bandwidth
- capacity
- data reuse
7. Global memory
Learn:
- memory transactions
- coalescing
- aligned access
- sequential access
- strided access
Bad:
1Thread 0 → address 0 2Thread 1 → address 1000 3Thread 2 → address 2000
Better:
1Thread 0 → address 0 2Thread 1 → address 1 3Thread 2 → address 2
8. Shared memory
Learn:
1__shared__ ,[object Object], data[,[object Object],];
Then:
- shared-memory tiling
- synchronization
- bank conflicts
- data reuse
9. Registers
Understand:
- register allocation
- register pressure
- register spilling
- relationship between registers and occupancy
Phase 4 — Parallel Algorithms
Now start implementing useful algorithms.
10. Elementwise kernels
Implement:
1Vector Add 2Vector Multiply 3Vector Scale 4Clamp 5ReLU 6SiLU 7GELU
Example:
1y = SiLU(x)
This teaches:
- indexing
- memory access
- simple parallelism
11. Reduction kernels
Extremely important for AI.
Implement:
1sum 2mean 3max 4min 5sum of squares
Then:
1Softmax 2LayerNorm 3RMSNorm
You'll learn:
- reductions
- warp operations
- shared memory
- synchronization
Phase 5 — Optimization
Now move from "make it work" to "make it fast."
12. Memory optimization
Learn:
1coalescing 2tiling 3data reuse 4cache behavior 5shared memory
13. Kernel fusion
Very important for AI.
Instead of:
1Kernel A 2 ↓ 3Global Memory 4 ↓ 5Kernel B 6 ↓ 7Global Memory 8 ↓ 9Kernel C
try:
1Fused Kernel 2 ↓ 3less memory traffic 4less launch overhead
Practice:
1Add + ReLU 2Multiply + Add 3Bias + Activation 4RMSNorm
14. Occupancy
Learn:
1occupancy 2register usage 3shared memory usage 4block size 5warp count
But remember:
Maximum occupancy does not necessarily mean maximum performance.
Phase 6 — Profiling
This should become part of every serious kernel project.
Learn:
Nsight Systems
For:
1CPU ↔ GPU 2kernel launches 3streams 4synchronization 5timeline
Nsight Compute
For individual kernels:
1occupancy 2memory throughput 3registers 4warps 5cache 6instructions 7Tensor Cores
PyTorch Profiler
For:
1PyTorch → CUDA kernels
Your workflow should become:
1Implement 2 ↓ 3Benchmark 4 ↓ 5Profile 6 ↓ 7Find bottleneck 8 ↓ 9Optimize 10 ↓ 11Benchmark again
Phase 7 — Matrix Multiplication
This is a major milestone.
15. Naive MatMul
Implement:
1C = A × B
Learn:
12D indexing
16. Tiled MatMul
Then implement:
1Global Memory 2 ↓ 3Shared Memory 4 ↓ 5Register computation 6 ↓ 7Output
Learn:
- tiling
- shared-memory reuse
- synchronization
- memory coalescing
17. Optimized GEMM
Learn:
1GEMM 2Tensor Cores 3mixed precision
Understand why:
1torch.matmul()
is extremely optimized.
Phase 8 — Triton
After CUDA fundamentals, learn Triton.
For your AI career, this is extremely valuable.
Learn:
1tl.program_id() 2tl.arange() 3tl.load() 4tl.store() 5masking 6blocks 7tiling 8reduction 9autotuning
Implement:
1Vector Add 2ReLU 3Softmax 4RMSNorm 5LayerNorm 6MatMul
Phase 9 — Transformer Kernels
Now connect your kernel knowledge directly to what you've already been learning.
18. Embedding
Understand:
1token ID 2 ↓ 3embedding lookup 4 ↓ 5GPU memory
Focus on memory access rather than complex arithmetic.
19. RoPE
Implement:
1Q 2K 3 ↓ 4RoPE 5 ↓ 6rotated Q/K
This is a good Triton project.
20. RMSNorm
Implement:
1x 2 ↓ 3mean(x²) 4 ↓ 5rsqrt 6 ↓ 7normalize 8 ↓ 9scale
This teaches:
- reduction
- numerical stability
- fusion
21. Softmax
Implement numerically stable softmax:
1x 2 ↓ 3max(x) 4 ↓ 5x - max(x) 6 ↓ 7exp 8 ↓ 9sum 10 ↓ 11divide
This is a very important kernel.
22. SwiGLU
Implement:
1gate_proj 2 ↓ 3 SiLU 4 ↓ 5 × 6 ↑ 7up_proj
This connects directly to the LLM architectures you've been studying.
Phase 10 — Attention
Now the serious part.
23. QKV
Understand:
1X 2 ↓ 3Q = XWq 4K = XWk 5V = XWv
Focus on GEMM and memory layout.
24. Scaled dot-product attention
Implement:
1QKᵀ 2 ↓ 3scale 4 ↓ 5softmax 6 ↓ 7× V
First implement the straightforward version.
Then optimize it.
Phase 11 — FlashAttention
This should be one of your major kernel-programming projects.
Learn:
1IO-aware algorithms 2tiling 3online softmax 4shared memory 5register reuse
Understand the fundamental idea:
1Don't unnecessarily materialize 2large intermediate attention matrices 3in global memory.
This is where CUDA optimization becomes directly connected to modern LLMs.
Phase 12 — Tensor Cores
Learn:
1FP32 2FP16 3BF16 4FP8 5INT8
Understand:
1CUDA cores 2vs 3Tensor Cores
Then learn:
1matrix multiply accumulate
and how libraries such as cuBLAS/cuBLASLt exploit GPU hardware.
Phase 13 — Quantized Kernels
For LLM inference:
1FP16 2 ↓ 3INT8 4 ↓ 5INT4
Learn:
- quantization
- dequantization
- scales
- zero points
- packed weights
- quantized GEMM
- memory bandwidth
Projects:
1INT8 Vector 2INT8 MatMul 3INT8 GEMM 4INT4 Weight-only MatMul
Phase 14 — LLM Inference Kernels
Now combine everything.
Learn:
KV Cache
1K → cache 2V → cache
Understand:
- memory layout
- cache growth
- paged KV cache
- memory bandwidth
Fused kernels
Study kernels for:
1RMSNorm + Linear 2RoPE 3QKV 4Attention 5MLP 6SwiGLU
Speculative decoding
Understand how inference kernels interact with:
1draft model 2target model 3KV cache 4GPU utilization
Phase 15 — Advanced GPU Systems
After you become comfortable with kernels:
CUDA Streams
1stream 1 2stream 2 3stream 3
Learn overlapping:
1computation 2+ 3memory transfer
CUDA Graphs
Understand how to reduce CPU/kernel-launch overhead for repeated workloads.
Multi-GPU
Then learn:
1NCCL 2all-reduce 3all-gather 4reduce-scatter
This becomes important for large-model training.
Your Project Ladder
Don't just read the topics. Build these:
1LEVEL 1 2──────────── 31. Vector Add 42. Vector Multiply 53. ReLU 64. SiLU 75. GELU 8 9LEVEL 2 10──────────── 116. Sum Reduction 127. Max Reduction 138. Softmax 149. RMSNorm 1510. LayerNorm 16 17LEVEL 3 18──────────── 1911. Naive MatMul 2012. Tiled MatMul 2113. Optimized MatMul 22 23LEVEL 4 24──────────── 2514. RoPE 2615. SwiGLU 2716. Fused MLP 2817. Fused RMSNorm 29 30LEVEL 5 31──────────── 3218. QKV 3319. Attention 3420. Optimized Attention 35 36LEVEL 6 37──────────── 3821. FlashAttention-style kernel 3922. Tensor Core GEMM 4023. FP8 kernel 4124. INT8 GEMM 4225. INT4 MatMul 43 44LEVEL 7 45──────────── 4626. KV Cache 4727. Paged KV Cache concepts 4828. Fused Transformer block 4929. LLM inference optimization 50 51LEVEL 8 52──────────── 5330. Multi-GPU kernels 5431. NCCL 5532. Distributed training 5633. End-to-end LLM performance optimization
What I recommend specifically for you
Because you've already been studying Transformers, Qwen, Kimi-style architectures, LoRA, ASR, PyTorch and model deployment, don't follow a generic beginner CUDA course for months.
Use this path:
1CUDA fundamentals 2 ↓ 3GPU memory 4 ↓ 5Warp programming 6 ↓ 7Reductions 8 ↓ 9Tiled MatMul 10 ↓ 11Profiling 12 ↓ 13Triton 14 ↓ 15RMSNorm 16 ↓ 17Softmax 18 ↓ 19RoPE 20 ↓ 21SwiGLU 22 ↓ 23Attention 24 ↓ 25FlashAttention 26 ↓ 27Tensor Cores 28 ↓ 29Quantization 30 ↓ 31KV Cache 32 ↓ 33LLM inference optimization
Your ultimate goal shouldn't be "I know CUDA."
It should be:
Given a slow Transformer operation, I can profile it, identify whether it is compute-, memory-, launch-, or synchronization-bound, write/modify a CUDA or Triton kernel, benchmark it, verify numerical correctness, and explain why it became faster.
That is the frontier-AI kernel-engineering skill you should target.