GPU portability for the world's most demanding AI

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

completion = client.chat.completions.create(
    model="google/gemma-3-27b-it",
    messages=[
        {
          "role": "user",
          "content": "Who won the world series in 2020?"
        },
    ],
)

print(completion.choices[0].message.content)

# total file size - 500MB.  This docker container deploys
across GPUs and CPUs, all without vendor libraries like CUDA

docker run --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_HUB_ENABLE_HF_TRANSFER=1" \
    --env "HF_TOKEN=" \
    -p 8000:8000 \
    docker.modular.com/modular/max-nvidia-base:nightly \
    --model-path meta-llama/Llama-3.3-70B-Instruct

alias M = C.shape[0]()
alias N = C.shape[1]()
alias K = A.shape[1]()

var warp_id = thread_idx.x // WARP_SIZE

warp_y = warp_id // (BN // WN)
warp_x = warp_id % (BN // WN)

C_warp_tile = C.tile[BM, BN](block_idx.y, block_idx.x).tile[WM, WN](
    warp_y, warp_x
)

mma_op = TensorCore[A.dtype, C.dtype, Index(MMA_M, MMA_N, MMA_K)]()

A_sram_tile = tb[A.dtype]().row_major[BM, BK]().shared().alloc()
B_sram_tile = tb[B.dtype]().row_major[BK, BN]().shared().alloc()

c_reg = (
    tb[C.dtype]()
    .row_major[WM // MMA_M, (WN * 4) // MMA_N]()
    .local()
    .alloc()
    .fill(0)
)

for k_i in range(K // BK):
    barrier()

    A_dram_tile = A.tile[BM, BK](block_idx.y, k_i)
    B_dram_tile = B.tile[BK, BN](k_i, block_idx.x)

    copy_dram_to_sram_async[thread_layout = Layout.row_major(4, 8)](
        A_sram_tile.vectorize[1, 4](), A_dram_tile.vectorize[1, 4]()
    )
    copy_dram_to_sram_async[thread_layout = Layout.row_major(4, 8)](
        B_sram_tile.vectorize[1, 4](), B_dram_tile.vectorize[1, 4]()
    )

    async_copy_wait_all()
    barrier()

    A_warp_tile = A_sram_tile.tile[WM, BK](warp_y, 0)
    B_warp_tile = B_sram_tile.tile[BK, WN](0, warp_x)

    @parameter
    for mma_k in range(BK // MMA_K):

        @parameter
        for mma_m in range(WM // MMA_M):

            @parameter
            for mma_n in range(WN // MMA_N):
                c_reg_m_n = c_reg.tile[1, 4](mma_m, mma_n)

                A_mma_tile = A_warp_tile.tile[MMA_M, MMA_K](mma_m, mma_k)
                B_mma_tile = B_warp_tile.tile[MMA_K, MMA_N](mma_k, mma_n)

                a_reg = mma_op.load_a(A_mma_tile)
                b_reg = mma_op.load_b(B_mma_tile)

                var d_reg_m_n = mma_op.mma_op(
                    a_reg,
                    b_reg,
                    c_reg_m_n,
                )

                c_reg_m_n.copy_from(d_reg_m_n)

@parameter
for mma_m in range(WM // MMA_M):

    @parameter
    for mma_n in range(WN // MMA_N):
        var C_mma_tile = C_warp_tile.tile[MMA_M, MMA_N](mma_m, mma_n)
        var c_reg_m_n = c_reg.tile[1, 4](mma_m, mma_n)
        mma_op.store_d(C_mma_tile, c_reg_m_n)