Serveur MCP VoltageGPU
Commit 6 nov. 2026Déployez et gérez des VM confidentielles et des pods GPU standard avec Intel TDX et l'attestation NVIDIA via une interface en ligne de commande ou un SDK Python.
Aussi connu sous VoltageGPU/VOLTAGEGPU-CLI
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- 6 nov. 2026
- Licence
- MIT
Qu'est-ce que VoltageGPU MCP ?
VoltageGPU est un serveur MCP. Déployez et gérez des VM confidentielles et des pods GPU standard avec Intel TDX et l'attestation NVIDIA via une interface en ligne de commande ou un SDK Python.
Résumé généré à partir du README du projet
À propos du MCP server VoltageGPU
VOLTAGEGPU-CLI fournit une interface en ligne de commande et un SDK Python pour déployer et gérer des ressources de calcul haute performance. Les développeurs peuvent provisionner des pods GPU standard pour des charges de travail générales ou des VM confidentielles utilisant Intel TDX et l'attestation NVIDIA pour les tâches sensibles. L'outil permet un contrôle précis sur la sélection des machines, incluant le filtrage par modèle de GPU ou par prix maximum.
Les utilisateurs interagissent avec le service via la commande voltagegpu ou l'alias plus court vgpu. La configuration nécessite une clé API et des clés SSH enregistrées pour un accès sécurisé. Le système prend en charge l'exécution locale via un MCP server pour les agents IA, qui utilise stdio pour fournir des outils tels que ls, up, ps et exec. Cette configuration locale garantit que les clés SSH privées restent sur la machine de l'utilisateur tout en permettant aux agents de gérer l'infrastructure. La facturation est calculée à la seconde, et le CLI inclut des protections intégrées comme les paramètres obligatoires de durée de vie (time-to-live) pour éviter les coûts imprévus.
Ce que vous pouvez faire avec
- 1Déployer le GPU le moins cher disponibleTrouvez et lancez la machine standard la plus abordable avec un modèle de GPU spécifique.
- 2Vérifier l'intégrité du matériel confidentielExécutez une vérification d'attestation pour vous assurer qu'une VM confidentielle fonctionne sur un matériel de confiance Intel TDX et NVIDIA.
- 3Automatiser l'infrastructure via des agents IAConnectez un client IA au MCP server local pour gérer les pods à l'aide d'outils comme list, up et remove.
- 4Exécuter des commandes à distance en toute sécuritéExécutez une commande spécifique sur un pod en cours d'exécution et récupérez le code de sortie sans passer par un shell interactif.
Extrait du README du projet. Chaque cas d'utilisation est vérifié par rapport à une citation du document.
Configuration
VOLT_API_KEY
| Nom | Description | Type |
|---|---|---|
| VOLT_API_KEY | — |
Vérifications automatisées
Exécuté le 15 nov. 2026Rapport complet →Pas encore vérifié. Un serveur nécessite un score de 80, aucune faille sérieuse et suffisamment de données pour que nous puissions effectuer les vérifications. Fonctionnement des vérifications · Ce qu'il faut corriger pour gagner des points
- Dépôt source lié et accessiblevoltagegpu/voltagegpu-cli is public and reachablegithub.com/voltagegpu/voltagegpu-cli ↗Réussi
- Le nom du package correspond au dépôt sourceNon vérifié
- Provenance du build (attestation Sigstore)Non vérifié
- Licence open source (OSI)MITspdx.org/licenses/MIT.html ↗Réussi
- Commit ou version publiée au cours de l'année écouléeLast commit or release 2026-11-06github.com/voltagegpu/voltagegpu-cli ↗Réussi
- OpenSSF ScorecardNon vérifié
- Nom du package non proche d'un package populaireNon vérifié
- Aucun avis critique dans le package ou ses dépendancesNon vérifié
- Aucune instruction cachée destinée au modèle0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersRéussi
- Les outils destructifs sont étiquetésNon vérifié
- Le point de terminaison distant répond à la poignée de main MCPNon vérifié
README
Voir sur GitHubvoltagegpu-cli
Deploy and manage VoltageGPU machines from your terminal or from Python:
- Confidential VMs: Intel TDX trust domains with an NVIDIA GPU, only your SSH keys on the machine, and an attestation you generate and verify yourself.
- Standard pods: whole GPU machines without an enclave, for workloads that are not sensitive.
Billing is per second. The first hour is charged when you deploy; when you stop a standard pod, the unused part of that hour goes back to your balance.
pip install voltagegpu-cli
Python 3.10 or newer. The package installs the voltagegpu command (volt is
an alias kept from earlier releases).
1. Log in
Create an API key at https://voltagegpu.com/api-keys (it is shown once), then:
voltagegpu login # prompts for the key, hidden, checks it, saves it to ~/.volt/config.ini (mode 0600)
# or, for CI and scripts:
export VOLT_API_KEY=volt_...
Add the SSH key you will connect with (defaults to ~/.ssh/id_ed25519.pub):
voltagegpu ssh-keys add
voltagegpu ssh-keys list
2. Pick a machine
voltagegpu machines # Confidential VM tiers, then standard machines, cheapest first
voltagegpu machines --standard --gpu A6000 # filter by GPU model
voltagegpu machines --standard --max-price 1 --json
voltagegpu machines --confidential
voltagegpu templates # images for standard pods (PyTorch by default)
3a. Deploy a standard pod
voltagegpu deploy standard --machine <machine_id>
voltagegpu deploy standard --cheapest --gpu 4090 --wait # cheapest matching machine, wait for SSH
The CLI shows the price and asks before charging. In scripts, add --yes.
If your account has several SSH keys, choose one with --ssh-key <key_id>.
Other options: --template <template_id>, --jupyter, --ports 3,
--auto-terminate 8 (hours), --name my-pod.
3b. Deploy a Confidential VM
voltagegpu deploy confidential --tier rtx6000b-small --wait
voltagegpu deploy confidential --tier h100-small --hardstop 4 --port 8000 --port 8443/tcp
Every SSH key on your account is bound to the VM when it is created; a VM cannot be given a key afterwards, so add yours first. The sudo password is printed once and is not kept anywhere. The command also prints how to generate the attestation inside the VM.
4. Connect, inspect, stop
voltagegpu pods list
voltagegpu pods wait <pod_id> # until SSH answers
voltagegpu ssh <pod_id> # interactive shell
voltagegpu ssh <pod_id> -- nvidia-smi -L # run one command
voltagegpu ssh <pod_id> --print # just print the ssh command
voltagegpu pods show <pod_id>
voltagegpu pods stop <pod_id> # releases it: billing stops, the disk is deleted
voltagegpu balance
Fresh pods present a new host key. In non-interactive scripts you can accept it
on first use with -o StrictHostKeyChecking=accept-new.
5. Verify a Confidential VM
Inside the VM you produce an evidence bundle (the deploy output prints the exact commands). On your own machine:
pip install "voltagegpu-cli[verify]" # installs voltage-verify
voltagegpu verify bundle.json --challenge <hex-you-issued> --gpus 1
voltagegpu verify bundle.json --challenge <hex-you-issued> --offline --accept-offline-tcb
voltagegpu verify runs voltage-verify,
which checks the Intel TDX quote up to Intel's root and the NVIDIA GPU
attestation tokens, without trusting VoltageGPU. Online, the TCB status and
revocation are checked against Intel's collateral of today. --offline uses
the Intel collateral inside the bundle: the verdict names that limit and the
exit code is 3, unless --accept-offline-tcb (voltage-verify 0.3.0 or later).
voltagegpu verify returns voltage-verify's own exit code.
Scripting
Every list command takes --json; deploys and stops take --yes.
# Deploy the cheapest standard machine, wait for SSH, run a job, release it.
POD=$(voltagegpu deploy standard --cheapest --max-price 1 --yes --json | jq -r .deploy.pod_id)
voltagegpu pods wait "$POD"
voltagegpu ssh "$POD" -o StrictHostKeyChecking=accept-new -- nvidia-smi
voltagegpu pods stop "$POD" --yes
Exit codes: 0 success, 1 API or runtime error, 2 usage error or missing
confirmation.
vgpu: the short command for scripts and AI agents
Installed with the package, next to voltagegpu. The key comes from
VOLT_API_KEY or VOLTAGEGPU_API_KEY (or voltagegpu login) and is never
printed. Give an agent a limited key (scopes, its own machines only, a
daily budget) from https://voltagegpu.com/api-keys, never your full key.
vgpu ls --json # every offer, cheapest first
vgpu ls --kind vm --gpu H100 # vm | cpu | standard | baremetal | sandbox
vgpu up rtx6000b-small --name train1 --ttl 2h -y --json
vgpu ps --json
vgpu exec train1 "nvidia-smi" # returns the remote exit code
vgpu exec train1 -d "python train.py" # background: prints PID and log path
vgpu scp train1 /home/ubuntu/out.tgz ./out.tgz # --up to copy the other way
vgpu attest train1 # Confidential VM or Confidential CPU only
vgpu rm train1 -y
vgpu whoami # scopes, budgets, spend, balance
vgpu mcp # local MCP server (stdio) for agents
--ttl is mandatory and becomes the auto-stop of each product, rounded up,
refused locally when out of bounds: Confidential VM and CPU up to 720h,
standard pods 1h to 720h (whole hours), dedicated machines 1h to 72h (whole
hours), sandboxes up to 7d. up asks for confirmation unless -y is given,
and fails without -y when not run in a terminal. A deploy that gets no
answer is never retried: run vgpu ps first.
exec, scp and attest use your local SSH key (-i, or VGPU_SSH_KEY).
The first connection to a machine records its host key in
~/.volt/known_hosts.d/<id> (trust on first use); a changed key is refused.
Names given at up are kept in ~/.volt/vgpu-names.json; ids work too.
Confidential VMs and CPU tiers bind the SSH keys already on the account.
A standard GPU binds one key: the account's only key, or --ssh-key-id ID
when the account has several (voltagegpu ssh-keys list shows the ids).
Dedicated machines and sandboxes take key ids: by default every key of the
account, but a key limited to its own machines cannot read them, so pass
--ssh-key-id ID (repeatable). For such a key whoami and voltagegpu balance show the balance as hidden: only a full key reads it.
vgpu attest draws a challenge on your machine, runs the published proof
script in the machine (two-proofs-in-the-vm.sh for a Confidential VM,
snp-proof-in-the-vm.sh for a Confidential CPU tier, both embedded and pinned
by SHA-256), copies the archive back and verifies it here: voltage-verify
for Intel TDX + NVIDIA (pip install 'voltagegpu-cli[verify]'). --offline
verifies without network, with the Intel collateral inside the bundle: the TCB
and revocation are then not checked against Intel today, so the result stays
VERIFIED but names that limit (limits in --json) and exits with 10,
unless --accept-offline-tcb. For SEV-SNP: the report is requested at --vmpl (0 by
default, sent as SNP_VMPL; if VMPL 0 is refused, the script says so and asks
again at 1, and the result reports it), REPORT_DATA equals the challenge, the
report policy forbids debug and its VMPL field is the level requested (the
guest kernel uses VMPCK0, consistent with VMPL 0, no SVSM), then the AMD chain and the VCEK are
fetched again from AMD into a new folder (snpguest 0.11.0 fetch ca / fetch vcek on your machine), the ARK must be one of the AMD roots pinned in the
script, and snpguest verify certs / verify attestation run with those
certificates, never the ones in the archive (they come from the VM). Without
a local snpguest, or when the AMD key server cannot be reached, the result
is INCOMPLETE, never VERIFIED. Standard pods, dedicated machines and sandboxes have no tenant
attestation and are refused.
| Exit code | Meaning |
|---|---|
| 0 | ok |
| 1 | error (including a deploy that got no answer) |
| 2 | usage: bad option, --ttl out of bounds, missing -y |
| 3 | API key missing, malformed or refused |
| 4 | API key scope missing (API_KEY_SCOPE_MISSING), or account resource refused to a key limited to its own machines (API_KEY_ACCOUNT_RESOURCE) |
| 5 | not found (machine or tier) |
| 6 | balance too low or API key budget exceeded (API_KEY_BUDGET_EXCEEDED, HTTP 402): stop, nothing was charged |
| 7 | unavailable: sold out, over --max-price, rate limited, offer closed |
| 8 | attestation failed |
| 9 | attestation incomplete, or a local tool is missing (ssh, scp, voltage-verify) |
| 10 | attestation verified with a limit: --offline, TCB and revocation not checked against Intel today (--accept-offline-tcb accepts it; local MCP server: ATTESTATION_LIMITED) |
With --json, an error is also printed on stdout as
{"error": {"code", "message", "exit_code", ...}}.
Local MCP server
vgpu mcp serves the tools ls, up, ps, exec, scp, rm, attest,
inference and whoami over stdio. It runs on your machine because exec,
scp and attest use your SSH private key, which never leaves it. Same
guards as the command line: up requires ttl and max_hourly_price, rm
requires confirm: true, a 402 comes back as API_KEY_BUDGET_EXCEEDED with
"do not retry", the key is never part of an answer, and the sudo password of
a Confidential VM is never handed to the agent. inference calls the
OpenAI-compatible API with an inference key (vgpu_...) in
VOLTAGEGPU_API_KEY; a vgpu_ value there is never sent to the pod API,
which takes the volt_ key from VOLT_API_KEY.
claude mcp add voltagegpu -e VOLT_API_KEY=volt_... -- vgpu mcp # Claude Code
# Codex: ~/.codex/config.toml
[mcp_servers.voltagegpu]
command = "vgpu"
args = ["mcp"]
env = { VOLT_API_KEY = "volt_..." }
Python SDK
from volt.sdk import VoltageGPUClient
with VoltageGPUClient() as client: # VOLT_API_KEY or ~/.volt/config.ini
machine = client.list_machines("standard")[0] # cheapest first
result = client.deploy_standard(machine.id, name="my-pod")
pod = client.wait_until_reachable(result.pod_id)
print(pod.ssh_command)
print(client.release_pod(result.pod_id).prepaid_refund)
A complete example is in examples/deploy_standard_pod.py.
REST API
The CLI is a thin client of the public API, documented at https://docs.voltagegpu.com/api-reference. The same deploy with curl:
curl -X POST https://api.voltagegpu.com/api/volt/pods \
-H "X-API-Key: $VOLT_API_KEY" -H "Content-Type: application/json" \
-d '{"provider":"standard","name":"my-pod","machine_id":"<machine_id>"}'
curl https://api.voltagegpu.com/api/volt/pods -H "X-API-Key: $VOLT_API_KEY"
Configuration
| Setting | Where |
|---|---|
| API key | --api-key of login, VOLT_API_KEY (or VOLTAGEGPU_API_KEY), then ~/.volt/config.ini |
| API base URL | VOLT_BASE_URL (https only, voltagegpu.com hosts unless VOLT_ALLOW_CUSTOM_BASE_URL=1; default https://api.voltagegpu.com/api) |
| Debug | VOLT_DEBUG=1 shows tracebacks |
voltagegpu config shows where the key comes from, masked.
Security
- The key is sent only over HTTPS, in the
X-API-Keyheader; redirects are never followed and certificates are always verified. - The key is never printed.
~/.volt/config.iniis created readable by you only, andvoltagegpu configwarns if its permissions are too open. - The SSH command returned by the API is parsed into user, host and port, only that rebuilt form is ever shown, and ssh is started without a shell (and never from the current directory), so a tampered answer cannot run a command on your machine. Server text is printed literally, without terminal escapes.
ssh-keys addrefuses private keys.
Report a vulnerability to contact@voltagegpu.com.
Upgrading from 1.2
volt cc inventory/volt cc deploy: Confidential containers were retired on 2026-10-01. Usevoltagegpu machines --confidentialandvoltagegpu deploy confidential --tier <tier>.volt pods stopnow releases the pod and reports the refund.volt account balanceis nowvoltagegpu balance.
Links
- Docs: https://docs.voltagegpu.com/cli
- API reference: https://docs.voltagegpu.com/api-reference
- API keys: https://voltagegpu.com/api-keys
- Changelog: CHANGELOG.md
MIT License.
Questions sur VoltageGPU MCP
Comment VoltageGPU est-il facturé ?
La facturation s'effectue à la seconde. Pour la première heure, vous êtes débité lors du déploiement, mais si vous arrêtez un pod standard, la partie non utilisée de cette heure est créditée sur votre solde.
Comment me connecter au CLI ?
Vous pouvez créer une clé API sur leur site web, puis exécuter 'voltagegpu login' pour saisir la clé, ou exporter la variable d'environnement VOLT_API_KEY pour vos scripts.
Puis-je utiliser le MCP server de VoltageGPU avec Claude ?
Oui, vous pouvez l'ajouter à Claude Code en utilisant la commande 'claude mcp add voltagegpu -e VOLT_API_KEY=volt_... -- vgpu mcp'.
Quelle est la différence entre les VM confidentielles et les pods standard ?
Les VM confidentielles utilisent les domaines de confiance Intel TDX avec des GPU NVIDIA et permettent une auto-attestation, tandis que les pods standard sont des machines GPU complètes sans enclave pour les charges de travail non sensibles.