claude

gpu-vllm-g6-2b-w4a16

EC2 G6 DevOps for vLLM 0.30.0 serving google/gemma-4-E2B-it-qat-w4a16-ct (Gemma 4 E2B) on one NVIDIA L4 (Ada, SM 8.9): instance lifecycle, G-family quotas, x86_64 GPU DLAMI resolution and SRE diagnostics via the gpu-vllm-g6-2b-w4a16 MCP server. The raw-EC2 counterpart of a sagemaker-gemma SageMaker run, with matching vLLM version and settings. Forked from gpu-vllm-g6-2b 2026-09-29. Has served nothing.

3 on GitHubby xbill9v0.1.0Apache-2.0updated yesterday
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Plugin gpu-vllm-g6-2b-w4a16 from marketplace gemma4-dev in xbill9/gemma4-dev. Commands read from the repo's manifest — yesterday.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (1)

  • gpu-vllm-g6-2b-w4a16-management

    Manage AWS EC2 G6 capacity (x86_64 + NVIDIA L4, Ada SM 8.9) and vLLM serving of google/gemma-4-E2B-it-qat-w4a16-ct (Gemma 4 E2B), the raw-EC2 counterpart of a SageMaker run. Use when the user asks about provisioning, launching, listing, or terminating G6 instances for this model, running or debuggin

MCP servers (1)

  • gpu-vllm-g6-2b-w4a16

    runs: python3 ${CLAUDE_PLUGIN_ROOT}/skills/gpu-vllm-g6-2b-w4a16-management/mcp/server.py

View source on GitHub

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Compute Engine DevOps for the PyTorch/XLA Gemma 4 E2B rig: flex-start ct6e-standard-1t provisioning, capacity probing, quotas, cost estimates, and SRE diagnostics via the gce-pytorch-v6e1-2b MCP server. Serves google/gemma-4-E2B-it on one TPU v6e chip through public torch_xla (device "xla"), with the xprof + tensorboard profiling kit installed at boot. Forked from tpu-pytorch-v6e1-2b 2026-08-29 and migrated off the Cloud TPU API onto gcloud compute instances.

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EC2 G5g DevOps for the pure-JAX Gemma 4 E2B rig: instance lifecycle, G-family quotas, arm64 GPU DLAMI resolution, JAX-on-Turing verification, and SRE diagnostics via the gpu-jax-g5g-2b MCP server. Serves google/gemma-4-E2B-it on an NVIDIA T4G (Turing, SM 7.5) behind an AWS Graviton2 host — no build, no CUDA toolkit, no Triton patch, because jax[cuda12] publishes aarch64 wheels that already carry sm_75.

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EC2 G6 DevOps for the pure-JAX Gemma 4 E2B rig: instance lifecycle, G-family quotas, x86_64 GPU DLAMI resolution, JAX-on-Ada verification, and SRE diagnostics via the gpu-jax-g6-2b MCP server. Serves google/gemma-4-E2B-it on an NVIDIA L4 (Ada, SM 8.9, 23034 MiB). Forked from gpu-jax-g5g-2b 2026-08-28 to move the same engine off Turing onto a native bfloat16 datapath: measured the same day at 48.3-48.5 tok/s against the T4G's 12.80 at an identical config, with the 54% dtype-conversion tax at 0.0% and decode at the bandwidth roofline.

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