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Bump MLX to the latest selected upstream ref and update the MLX/imagegen wrappers and tests for the new API behavior. Fix the CUDA MLX archive so runtime NVRTC kernels work after deployment: package CUTE/CUTLASS headers, include the CUDA runtime header closure, and stage a coherent CUDA-toolkit-matched CCCL tree instead of MLX's fetched CCCL for CUDA payloads. The previous archive could build successfully but crash at runtime due to missing or incompatible JIT headers.
MLX Memory Management
| This package will get consolidated with x/ml/backend/mlx in the future.
Automatic Tracking
All arrays are automatically tracked when created. On Eval(), non-kept arrays are freed.
API
result := mlx.Matmul(x, w) // arrays automatically tracked
mlx.Eval(result) // free non-kept, eval result (auto-kept)
Key Functions
mlx.Eval(outputs...)- free non-kept arrays, then evaluate (outputs auto-kept)mlx.AsyncEval(outputs...)- async version of Eval (outputs auto-kept)mlx.Keep(arrays...)- mark arrays to survive cleanup (for weights, caches)array.Free()- mark array for cleanup on next Eval
Loop Pattern
for step := 0; step < maxTokens; step++ {
logits := model.Forward(token, caches)
oldToken := token
token = sample(logits)
// Keep cache state across iterations
for _, c := range caches {
mlx.Keep(c.State()...)
}
oldToken.Free() // mark for cleanup
mlx.AsyncEval(token) // frees old, evals new
}
Notes
Eval()andAsyncEval()auto-keep their outputsFree()marks for cleanup - actual free happens during next Eval- Use
Keep()for weights and cache state that must survive multiple Eval cycles - Arrays created inside compiled closures are managed by MLX, not tracked