Manage AWS Inferentia2 EC2 and Neuron vLLM inference.
3 on GitHubby xbill9v0.2.0Apache-2.0updated yesterday
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin tpu-pytorch-inf2-2b 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 (2)
tpu-management
Manage Google Cloud TPU capacity and Gemma 4 vLLM serving on TPU VMs. Use when the user asks about provisioning, finding, listing, or destroying TPUs / queued resources / flex-start VMs, starting or debugging vLLM on TPU (v6e, v5p, v5e), TPU quotas and zones, TPU cost estimates, benchmarking TPU ser
tpu-pytorch-inf2-2b-management
Provision and operate AWS EC2 Inferentia2 instances and Neuron vLLM serving through the tpu-pytorch-inf2-2b MCP server.
Manage Google Cloud TPU capacity and Gemma 4 serving from Rust on Compute Engine TPU VMs: flex-start provisioning, capacity probing, quotas, cost estimates, and SRE diagnostics via the gce-jaxrust-v6e1-2b MCP server. The engine is Rust — rlx's JAX-shaped IR lowered to HLO and executed through libtpu's PJRT plugin, no Python in the serving path. Forked from tpu-jax-v6e1-2b 2026-08-28; nothing provisioned or measured on this path yet.
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.
EC2 G4dn DevOps for the pure-JAX Gemma 4 E2B rig: instance lifecycle, G-family quotas, x86_64 GPU DLAMI resolution, JAX-on-Turing verification, and SRE diagnostics via the gpu-jax-g4dn-2b MCP server. Serves google/gemma-4-E2B-it on an NVIDIA T4 (Turing, SM 7.5, 15360 MiB measured). Forked from gpu-jax-g5g-2b 2026-08-28 as the A/B control that separates Turing from Graviton2, and first served 2026-08-29: decode 13.1 tok/s against the T4G's 13.10, weight bytes identical to the byte, and the xprof profile reproducing 54.4% dtype conversion / 32.8% fp32 GEMV / 0.0% TensorCore — so the sibling's dtype tax is a chip finding, not a host one.
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.
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.
EC2 G5g DevOps for the Rust-driven-XLA Gemma 4 E2B rig: instance lifecycle, G-family quotas, arm64 GPU DLAMI resolution, Turing verification, and SRE diagnostics via the gpu-jaxrust-g5g-2b MCP server. Forked from gpu-jax-g5g-2b 2026-08-28 to move the engine off CPython onto Rust. The control plane is complete and measured nothing yet — the Rust engine is not written, and the rig currently runs the inherited Python JAX engine as its fallback and parity oracle.
Connect Claude to Prowler Cloud through the Prowler MCP server. Reads providers, scans, findings, compliance results and resources for AWS, Azure, GCP, Kubernetes, Microsoft 365, GitHub and the other providers listed at https://registry.prowler.com, and returns remediation guidance for failed checks. Writes are limited to Prowler Cloud itself: triggering scans, finding triage, mute rules and the mutelist, providers and integrations. Your cloud resources are not changed by the connector.
Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), cost optimization, and Well-Architected Framework reviews across all six pillars.
27 skills · 1 MCP server
aws/agent-toolkit-for-aws 3,477
Maintain tpu-pytorch-inf2-2b?
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