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cuBLAS & Quantized FP8 Matrix Multiplication in Rust

💎 Tensor Cores, WMMA & Hardware Acceleration⏱ 18 min⭐ 150 BASE XP⌨ HANDS-ON LAB

Zero-Overhead cuBLAS Bindings

While custom PTX is vital for unique activations, dense GEMM (General Matrix Multiply) operations are best delegated to cuBLAS, NVIDIA's hand-tuned assembly library. cudarc exposes native cuBLAS bindings in safe Rust:

use cudarc::cublas::{CudaBlas, GemmConfig, Gemm};

pub fn run_cublas_gemm(
    blas: &CudaBlas,
    a: &CudaSlice,
    b: &CudaSlice,
    c: &mut CudaSlice,
    m: usize, n: usize, k: usize,
) -> Result<(), Box> {
    let cfg = GemmConfig {
        alpha: 1.0,
        beta: 0.0,
        m: m as i32,
        n: n as i32,
        k: k as i32,
        lda: k as i32,
        ldb: n as i32,
        ldc: n as i32,
    };

    // Invokes cuBLAS SGEMM with zero pointer allocation overhead
    unsafe { blas.gemm(cfg, a, b, c) }?;
    Ok(())
}
⌨ HANDS-ON LABExecute FP8 Tensor Core GEMM in Rust
⭐ +200 XP

Execute an ultra-high performance FP8 matrix multiplication using cuBLASLt bindings in Rust.

1Run 4096x4096x4096 FP8 GEMM benchmark.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
QUERY 1 // 1
Why is FP8 (e4m3fn) precision widely adopted for transformer inference on modern NVIDIA GPUs?
It cuts memory bandwidth consumption by 50% compared to FP16 while doubling Tensor Core math throughput with near-zero perplexity loss
It eliminates the need for GPU drivers
It allows models to run without CUDA
It converts text tokens into audio waveforms automatically