oxillama-quant

Quantization kernels for all GGUF quantization types used in LLM inference.

Part of the OxiLLaMa workspace — a Pure Rust LLM inference engine.

Status

Version: 0.1.2 — Tests: 382 passing

What's New in v0.1.2

IQ3_S and IQ3_XXS codebook tables added in v0.1.2

What It Provides

25 quantization types: Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1, Q2_K, Q3_K_S/M/L, Q4_K_S/M, Q5_K_S/M, Q6_K, IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, IQ3_XXS, IQ3_S, IQ4_NL, IQ4_XS, Q1_0_G128
SIMD-accelerated paths: AVX-512, AVX2 (27 kernels), ARM NEON (27 kernels), and portable scalar fallback; 11 AVX-512 kernels
Fused dequant+GEMM for Q4_0 and Q4_K on AVX2 and NEON — single-pass matmul with no intermediate f32 scratch buffer
matvec_q8_fused trait method on QuantKernel with scalar default impl and SIMD overrides
oxiblas float GEMM fallback for F16/BF16/F32 tensors via oxiblas::gemv_f32/f16
Parallel dequantization via rayon (feature-gated)
Property-based test coverage for all kernels

Key Types

Type	Description
`QuantKernel`	Trait implemented by every quantization format
`QuantTensor`	Owned buffer of quantized blocks + element type tag
`dispatch_kernel`	Runtime dispatch to the correct kernel for a `GgmlType`
`DequantizeSlice`	Zero-allocation dequantize into a caller-provided `f32` slice

Usage

use oxillama_quant::{QuantResult, dispatch_dequantize};
use oxillama_gguf::GgmlType;

fn dequantize_tensor(raw: &[u8], dtype: GgmlType, out: &mut Vec<f32>) -> QuantResult<()> {
    dispatch_dequantize(dtype, raw, out)?;
    Ok(())
}

Feature Flags

Feature	Default	Description
`parallel`	yes	Enable Rayon-based parallel dequantization
`simd-avx2`	no	AVX2 256-bit SIMD kernel path
`simd-avx512`	no	AVX-512 512-bit SIMD kernel path
`simd-neon`	no	ARM NEON 128-bit SIMD kernel path

License

Apache-2.0 — COOLJAPAN OU (Team Kitasan)

oxillama-quant 0.1.2