Comments (1)
On my tests GGML gemm is slower.
Like ggml ~ 1.5 TFlops, and mlx (quite close to PyTorch) ~ 3.5 TFlops on M1 Pro (32 Gb).
Of course llama is not only gemm, but you can estimate
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Related Issues (20)
- [Feature] Multi-Machine Support for Distributed Inference HOT 4
- [BUG] mlx_lm issue with Phi-3 fine tuned model: adding and repeating weird tokens HOT 2
- [FEATURE] in keras LayerNorm by default is apply to last dimension only HOT 9
- [BUG] in-place updating of array slice unexpectedly fails due to broadcasting problem HOT 2
- [BUG] Matmul gives wrong output for large sizes HOT 5
- [BUG] broadcast of scalar array in last dimension fails after #1035
- [BUG] Unable to install mlx on MacbookPro M3Pro with MacOS 14.4.1 HOT 1
- [FEATURE] how to return mlx intermediate layer output similarly to Keras HOT 2
- [BUG] cannot replicate a keras model into mlx when I reuse keras pretrained weights
- [BUG] EOS terminator for mlx_lm generate function HOT 1
- [BUG] libc++abi crash when using recurrent layer and transformer HOT 2
- [Feature] arctan2 HOT 3
- [BUG] arithmetic operations with numpy arrays are not commutative HOT 3
- [Feature] KANs HOT 1
- No module named 'mlx.core'; 'mlx' is not a package HOT 2
- 0.12.2 release was not completed HOT 8
- [FEATURE REQUEST] mx.grad doesn't alias argnums and argnames HOT 5
- [BUG] `np.ndarray` of bfloat16 using ml_dtypes is being interpreted as complex64
- [BUG] mlx crashes with msg - uncaught exception of type std::invalid_argument: [Scatter::eval_gpu] Does not support int64 HOT 4
- Is dlpack supported? HOT 12
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