Performance

MIND achieves exceptional performance through its innovative compiler architecture — ultra-fast compilation, 100% deterministic builds, and compile-time autodiff.

Scientific Benchmark Methodology (Jan 2026)

We measure pure compilation time by subtracting subprocess startup overhead from all measurements — ensuring fair, apples-to-apples comparison:

MIND Compilation Modes

mind compile (typecheck): ~100 µs

mind build (full IR): 187 µs

Competitors (Pure Compile)

PyTorch 2.0: 2,766 ms (14,769× slower)

JAX: 135 ms (2,699× slower)

Mojo: 757 ms (4,040× slower)

MIND: Unique Dual-Mode Compilation

MIND is the only ML compiler offering both typecheck-only (mind compile) and full IR generation (mind build) modes:

mind compile (~100 µs) — Static type checking and shape inference only. Ideal for rapid iteration during development.
mind build (187 µs) — Full IR generation with all optimizations. Used for deployment and benchmarking.

Mojo only has build (full LLVM) — no separate typecheck mode. PyTorch/JAX also only have full compilation. MIND is unique with both modes.

Live Benchmark Demo

Watch the scientific benchmark running live — measuring subprocess overhead and calculating pure compilation time for fair comparison:

All measurements run live on the same machine with equivalent tensor operations. No hardcoded values.

Compilation Speed: MIND vs PyTorch 2.0

Scientific comparison using subprocess overhead subtraction methodology (Jan 2026):

Compiler	Command	Pure Compile Time	vs MIND
MIND	`mind compile`	~100 µs	baseline (typecheck)
MIND	`mind build`	187 µs	baseline (full IR)
PyTorch 2.0	`torch.compile` (inductor)	2,766 ms	14,769× slower
JAX	`jax.jit`	135 ms	2,699× slower
Mojo	`mojo build`	757 ms	4,040× slower

Methodology: Subprocess Overhead Subtraction

Startup Overhead (subtracted)

MIND: ~1.0 ms

PyTorch: ~1,380 ms

JAX: ~463 ms

Mojo: ~57 ms

Why This Matters

Naive subprocess timing unfairly penalizes Python-based frameworks. Pure Compile = Total − Startup ensures fair comparison of actual compilation work.

Live measurements on Ubuntu Linux (Jan 2026). All tests run on same machine with equivalent tensor operations.

Deterministic Compilation

MIND guarantees 100% bit-level reproducibility — every compilation produces identical output, verified via SHA256 cryptographic hashing.

Test Program	Runs	Unique Hashes	Result
scalar_math	10	1	Deterministic
small_matmul	10	1	Deterministic
medium_matmul	10	1	Deterministic
mlp	10	1	Deterministic

40 total runs, 0% hash collision rate, 100% reproducibility. As of December 2025, MIND is one of the few ML compilers that guarantees bit-identical output across runs, machines, and time.

Compile-Time Autodiff

MIND generates gradient code once at compile-time, not on every training iteration. This eliminates per-iteration autodiff overhead entirely.

Program	MIND Cost	PyTorch Cost	Advantage
Simple Quadratic	38 µs (once)	51,100 µs (1000 iters)	1,345×
Small MLP	38 µs (once)	345,900 µs (1000 iters)	9,103×
Matmul Chain	38 µs (once)	428,800 µs (1000 iters)	11,284×

Key Insight

MIND's compile-time autodiff is 1,345-11,284× more efficient than runtime autodiff over 1000 training iterations. The gradient code is already generated — just execute it.

Optimization Levels

The compiler provides several optimization profiles:

Flag	Description	Deterministic
`--debug`	No optimizations, full debugging symbols	Yes
`--release`	Standard optimizations, deterministic	Yes
`--release --fast-math`	Maximum performance, relaxed floating-point	No

Compiler Optimizations

The MLIR-based pipeline applies several optimization passes:

Operator fusion — combines sequential operations to reduce memory traffic
Layout optimization — selects optimal memory layouts for target hardware
Dead code elimination — removes unused computations
Constant folding — evaluates compile-time-known expressions
Loop tiling — improves cache utilization for large tensors

Target Performance (CPU)

Benchmark targets for Core v1 operations on CPU:

Operation	Target vs OpenBLAS
MatMul [4096x4096]	1.0x - 1.5x
Conv2D	1.2x - 2.0x
Element-wise ops	1.0x - 1.2x
Reductions	1.0x - 1.3x

Compilation Speed: MIND vs Mojo

Scientific comparison using subprocess overhead subtraction methodology (Jan 2026). Mojo only offers mojo build (full LLVM compilation) — no separate typecheck mode like MIND's mind compile.

Compiler	Command	Pure Compile Time	MIND Speedup
MIND	`mind build`	187 µs	baseline
Mojo	`mojo build`	757 ms	4,040× slower

Why MIND Is Faster Than Mojo

MIND: Purpose-built Rust compiler, minimal dependencies, efficient IR design
Mojo: Full LLVM pipeline including library initialization (~57ms startup overhead)
Key difference: Mojo has no typecheck-only mode — mojo build always runs full LLVM compilation

Live benchmark using scientific methodology (subprocess overhead subtracted). |View benchmark source

Profiling

Built-in profiling support for performance analysis:

# Generate a trace profile
mindc run model.mind --profile=trace --output=trace.json

# CPU time breakdown
mindc run model.mind --profile=time

Memory Efficiency

Static memory planning eliminates runtime allocation overhead
Buffer reuse analysis minimizes peak memory usage
Optional memory pooling for real-time applications

Framework Comparison

Scientific comparison using subprocess overhead subtraction methodology (Jan 2026):

Framework	Compilation	Typecheck-Only Mode	Autodiff	Determinism
MIND	100-187 µs	Yes (~100 µs)	Compile-time	100% guaranteed
PyTorch 2.0	2,766 ms	No	Runtime tape	Not guaranteed
JAX (XLA)	135 ms	No	JIT transforms	Mostly deterministic
Mojo	757 ms	No (LLVM only)	External	Yes

Key Insight: As of January 2026, MIND is the only ML compiler offering dual-mode compilation (typecheck-only + full IR), achieving sub-200 µs compilation, 100% deterministic builds, and compile-time autodiff.

Mojo only has build (full LLVM) — no separate typecheck mode. PyTorch/JAX also only have full compilation. MIND is unique with both modes.

GPU Runtime Performance (Enterprise)

The Enterprise CUDA backend delivers production-grade GPU acceleration, benchmarked on RTX 4070 (SM_89, Ada Lovelace):

Metric	PyTorch 2.8	MIND Runtime	Improvement
Memory Allocation	46K/sec	8.3M/sec	180x faster
MatMul TF32 (4096x4096)	12.83 TFLOPS	17.32 TFLOPS	35% faster
MatMul FP16 (4096x4096)	23.82 TFLOPS	33.34 TFLOPS	40% faster
Elementwise Bandwidth	228 GB/s	250 GB/s	98% of peak

GPU runtime requires Enterprise license. Performance scales with GPU capabilities. Benchmarks verified December 2025.

Learn More

Running Benchmarks — Reproduce the results yourself
Performance FAQ — Common questions answered
Full Benchmark Results — Complete verified data
Performance Specification — Official spec document