[GGUF] Reduce peak RAM usage by casting dequantized tensors early during load

[UsamaKenway]

Optimizes memory usage when loading GGUF models by performing dtype casting immediately after dequantization.

While I was adding the support for Gemma4 in this PR #45296, i noticed this issue that the GGUF tensors are dequantized to float32 by default during the loading process, even if the user intends to load the model in float16 or bfloat16. For large models, this creates a significant RAM spike that can lead to Out Of Memory.

By passing the target torch_dtype directly into the loading utility, we can cast the tensors immediately after dequantization, effectively halving the peak RAM required for the state dict.

Benchmark Results (Gemma 4 26B IT q4_k_m)

I tested the peak RAM (Global Peak RSS) with and without this change using a separate branch for tracking:

- Without this PR (Float32 spike): ~118.7 GB Peak RSS
- With this PR (Early casting):    ~59.4  GB Peak RSS
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Saving:                            ~59.3  GB (50% reduction)

Tests

With the changes

(py312venv) usamakenway@Megatron: RUN_SLOW=1 pytest tests/quantization/ggml/test_ggml.py::GgufModelTests::test_gemma4_26b_it_q4_k_m -s                                                                                                                                                                                                                                                                                                                                                   

[RAM DEBUG] Global Peak RSS (High Water Mark): 2185.59 MB

[RAM DEBUG] Global Peak RSS (High Water Mark): 2197.88 MB

[RAM DEBUG] Global Peak RSS (High Water Mark): 2391.64 MB
Converting and de-quantizing GGUF tensors...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 658/658 [03:03<00:00,  3.59it/s]

[RAM DEBUG] Global Peak RSS (High Water Mark): 59428.81 MB
Loading weights: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 657/657 [00:00<00:00, 26841.26it/s]
PASSEDtests/quantization/ggml/test_ggml.py::GgufModelTests::test_gemma4_26b_it_q4_k_m [PASSED] 287.34s

Without the changes

(py312venv) usamakenway@Megatron:  RUN_SLOW=1 pytest tests/quantization/ggml/test_ggml.py::GgufModelTests::test_gemma4_26b_it_q4_k_m -s
                                                                                                                                       
tests/quantization/ggml/test_ggml.py::GgufModelTests::test_gemma4_26b_it_q4_k_m 
[RAM DEBUG] Global Peak RSS (High Water Mark): 2259.14 MB

[RAM DEBUG] Global Peak RSS (High Water Mark): 2270.75 MB

[RAM DEBUG] Global Peak RSS (High Water Mark): 2464.60 MB
Converting and de-quantizing GGUF tensors...: 100%|███████████████████████████████████████████████████████████████████████████████| 658/658 [05:46<00:00,  1.90it/s]

[RAM DEBUG] Global Peak RSS (High Water Mark): 118747.33 MB
Loading weights: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 657/657 [00:03<00:00, 195.35it/s]
PASSEDtests/quantization/ggml/test_ggml.py::GgufModelTests::test_gemma4_26b_it_q4_k_m [PASSED] 499.38s

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[Rocketknight1]

cc @SunMarc

[SunMarc]

Thanks, a couple of comments

[UsamaKenway]

@SunMarc Changes pushed. refactored the logic as suggested . Let me know if you spot anything else or if this is good to go.
Thank you.

[SunMarc]

Much better thanks a lot !

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