Implementation Blueprint

Prerequisites

# Verify GPU availability
nvidia-smi

# Install the latest vLLM with Mamba support
pip install vllm>=0.6.2

Production API Deployment (vLLM)

python -m vllm.entrypoints.openai.api_server \
    --model ibm-granite/granite-4.0-32b-instruct \
    --tensor-parallel-size 2 \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.90 \
    --trust-remote-code \
    --host 0.0.0.0

Local Run (llama.cpp)

# Run the hybrid Micro variant on CPU or GPU
./main -m granite-4.0-3b-h.Q4_K_M.gguf -n 512 --prompt "Explain the benefits of ISO-certified AI."

Scaling Strategy

• MoE Routing: For the 32B variant, monitor expert utilization to ensure balanced GPU load and maximize the 9B-active throughput benefit.
• Quantization: Utilize FP8 or W4A16 quantization to fit the 32B model into a single 24GB VRAM GPU while preserving 100% of the hybrid reasoning logic.
• Hybrid Context Handling: Leverage the Mamba layers for rapid pre-filling of massive document sets before switching to transformer logic for fine-grained retrieval.

Backup & Safety

• Certified Weights: Always cross-reference SHA256 hashes with IBM's official signed distributions to maintain ISO compliance.
• Safety Protocols: Implement a dedicated moderation layer (like Llama Guard) to audit Granite's high-speed outputs for enterprise policy alignment.
• Redundancy: Maintain a secondary "Micro" node as a hot-fallback to ensure minimal service availability during large-scale node maintenance.