Google Cloud expands NVIDIA GPU lineup with G4 VMs (RTX 6000 Blackwell)Google Cloud expands NVIDIA GPU lineup with G4 VMs (RTX 6000 Blackwell)

Google Cloud has taken its next step on GPU breadth with new G4 VMs powered by NVIDIA RTX 6000 Blackwell. The pitch: accessible, scalable GPUs for inference, vision, and industrial workloads without chasing scarce H100/H200 capacity.

What Google is adding

New instance family: G4 brings RTX 6000 Blackwell GPUs—aimed at high-throughput inference, graphics/vision, digital twins, and mixed enterprise AI workloads.
Easier capacity: Compared with data-center class parts, RTX 6000 BWs are typically easier to book at scale, smoothing procurement for teams that just need reliable inference/graphics horsepower.
Ecosystem fit: CUDA, TensorRT, Omniverse, and ISV stacks (CAD/CAM, robotics, inspection) should slot in without drama.

Where this lands vs the rest

On Google Cloud, H100/H200 target peak training and the largest inference fleets; L4 and A3 show up in mid-range inference and training. G4 fills a practical gap: visual compute + steady inference for enterprise and industrial AI without the budget shock. Expect these to show up in manufacturing QA, retail analytics, supply-chain vision, photoreal simulation, and RAG services that lean on fast embeddings + light generation.

What buyers should check

Throughput per $: Benchmark your models (fp8/fp16, batch sizes) against existing L4/A3 setups. Price-performance—not peak FLOPS—will decide migrations.
Networking + storage: Ensure PCIe/NVLink assumptions hold and pair with fast persistent disks or Filestore to avoid IO bottlenecks.
Licensing: Confirm ISV licensing for virtualized GPUs (creative apps, CAD, robotics) and whether concurrency caps affect your team.
Regional availability: If you’re EU/UK-hosted, check data-residency and nearest regions to minimize latency.

Bottom line

The AI boom isn’t just about flagship training clusters. For most enterprises, reliable GPU availability and sane cost curves matter more. G4 looks like a pragmatic on-ramp for teams stuck waiting on higher-end parts—or overpaying for them.