Mlir vs TVM table of comparison
Even though TVM and MLIR are frameworks designed for the optimized and lower high-level computation representation to efficient machine code, the design philosophy and the purpose are completely different and they operate on the other level of the stack.
Below is their key differences
| Feature | TVM | MLIR |
|---|---|---|
| Primary Goal | Optimized deep learning model deployment | Infrastructure for building reusable compiler frameworks |
| Scope | End-to-end deep learning compilation (from model to hardware) | General-purpose intermediate representation (IR) for compilers |
| IR Type | Uses Relay (high-level) and TIR (low-level) | Multi-level IR with dialects for different abstraction levels |
| Target Audience | Primarily for ML engineers and practitioners | Compiler developers working on new frontends or backends |
| Optimization Focus | Tensor optimizations, auto-tuning, scheduling | Multi-level representation, extensibility, lowering passes |
| Hardware Support | CPU, GPU, FPGA, and custom accelerators (via AutoTVM & TensorIR) | Acts as an intermediate layer for various compiler backends (e.g., LLVM, TVM, XLA, IREE) |
| Flexibility | Designed specifically for ML workloads | Can be used for ML but also supports general compiler use cases |
| Adoption | Used by AI frameworks for optimized model execution | Used in LLVM, TensorFlow, IREE, and other compiler projects |
TVM -> Dealing with the deployment of the deep learning model and requirement is that you need efficient execution on various hardware targes with auto tuning and scheduling optimization
MLIR -> When you are dealing with the building a compiler or required a flexible multi level IR infra to transform and lower computation
| 💡 Tip |
|---|
| TVM can be considered as end-to-end deep learning compiler stack who main aim is to focus on optimizing and deploying ML models efficiently across various hardware backends. |
| MLIR (Multi-Level Intermediate Representation) is a compiler framework whose main aim is to build reusable and extensible compiler infrastructures. |
| Overall both TVM and MLIR have different goal thats the reason they complement each other rather than competing. |
