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prepare: progressive F16 input deletion under RUSTYLLM_DELETE_F16_INP…
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#26:
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spec_decode_70b: model IDs env-configurable
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README: document M3 Pro 36 GB recipe and Q2K-target trick
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Add mixed quantization (attn vs MLP) + slurp-based GgufLayer
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RustyLLM: 70B inference in pure Rust on a single small GPU
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RustyLLM: 70B inference in pure Rust on a single small GPU
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RustyLLM: 70B inference in pure Rust on a single small GPU
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RustyLLM: 70B inference in pure Rust on a single small GPU
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RustyLLM: 70B LLM inference in pure Rust on a single small GPU
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RustyLLM: 70B LLM inference in pure Rust on a single small GPU
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RustyLLM: 70B inference in Rust on a single small GPU
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Async H2D F16 streaming: 1.43x over sync (1033s -> 723s)
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Bench Q4_0 hybrid: 2x slower than Q4_K, despite same bits/param
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README: 20 GPU + 60 CPU hybrid = 22.3s (81x over baseline)
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README: update benchmark table with hybrid GPU+CPU residency win
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Add Phase 3a spike: real 1.6 GiB layer async-uploaded, header offset …
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Hybrid GPU + CPU residency: 78x over CPU baseline, beats Ollama 1.5x
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Add Phase 2 spike: cudarc CudaSlice → candle Tensor bridge validated
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Add async H2D spike: validates cudarc multi-stream overlap (37% savings)
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Document q4_kernel microbench result: candle QMatMul wins on wide shapes
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README: add CUDA streaming benchmark — rustyllm CUDA beats Python Air…
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Initial commit
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StreamingMixtral::from_pretrained + StreamingFamily Mixtral arm
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Add CONTRIBUTING.md
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Add direct AVX2 Q4_0×F32 matmul kernel (q4_kernel.rs)
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