tandemn-tuna

name	tandemn-tuna
description	Deploy and serve LLM models on GPU. Compare GPU pricing. Launch vLLM on Modal, RunPod, Cerebrium, Cloud Run, Baseten, or Azure with spot instance fallback. OpenAI-compatible inference endpoint.
version	0.0.1
metadata	{"openclaw":{"requires":{"bins":["uv"],"anyBins":["aws","az"],"env":[]},"emoji":"🐟","homepage":"https://github.com/Tandemn-Labs/tandemn-tuna","install":[{"kind":"uv","package":"tandemn-tuna","bins":["tuna"]}]}}

Tuna — Deploy and Serve LLM Models on GPU Infrastructure

Tuna is a hybrid GPU inference orchestrator. It lets you deploy, serve, and manage LLM models (Llama, Qwen, Mistral, DeepSeek, Gemma, and any HuggingFace model) on serverless GPUs from Modal, RunPod, Cerebrium, Google Cloud Run, Baseten, or Azure Container Apps, with optional spot instance fallback on AWS via SkyPilot. Every deployment gets an OpenAI-compatible /v1/chat/completions endpoint.

The key idea: serverless GPUs handle requests immediately (fast cold start, pay-per-second) while a cheaper spot GPU boots in the background. Once spot is ready, traffic shifts there. If spot gets preempted, traffic falls back to serverless automatically. This gives you 3–5x cost savings over pure serverless with zero downtime.

Quick Start — Deploy a Model in 3 Commands

# 1. Install tuna
uv pip install tandemn-tuna

# 2. Deploy a model (auto-picks cheapest serverless provider for the GPU)
tuna deploy --model Qwen/Qwen3-0.6B --gpu L4 --service-name my-llm

# 3. Query your endpoint (shown in deploy output)
curl http://<router-ip>:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen/Qwen3-0.6B", "messages": [{"role": "user", "content": "Hello!"}]}'

For serverless-only (no spot, no AWS needed):

tuna deploy --model Qwen/Qwen3-0.6B --gpu L4 --serverless-only

All Commands

tuna deploy — Launch a model on GPU

Deploy a model across serverless + spot infrastructure. This is the main command.

tuna deploy --model <HuggingFace-model-ID> --gpu <GPU> [options]

Required arguments:

• --model — HuggingFace model ID (e.g., Qwen/Qwen3-0.6B, meta-llama/Llama-3-70b)
• --gpu — GPU type (e.g., T4, L4, L40S, A100, H100, B200)

Common options:

• --service-name — Name for the deployment (auto-generated if omitted)
• --serverless-provider — Force a specific provider: modal, runpod, cloudrun, baseten, azure, cerebrium (default: cheapest available)
• --serverless-only — Serverless only, no spot backend or router (no AWS needed)
• --gpu-count — Number of GPUs (default: 1)
• --tp-size — Tensor parallel size (default: 1)
• --max-model-len — Max sequence length (default: 4096)
• --spots-cloud — Cloud for spot GPUs: aws or azure (default: aws)
• --region — Cloud region for spot instances
• --concurrency — Override serverless concurrency limit
• --no-scale-to-zero — Keep at least 1 spot replica running
• --public — Make endpoint publicly accessible (no auth)
• --scaling-policy — Path to YAML with scaling parameters

Provider-specific options:

• --gcp-project, --gcp-region — For Cloud Run
• --azure-subscription, --azure-resource-group, --azure-region, --azure-environment — For Azure

Examples:

# Deploy Llama 3 on Modal with hybrid spot
tuna deploy --model meta-llama/Llama-3-8b --gpu A100 --serverless-provider modal

# Deploy on RunPod, serverless-only
tuna deploy --model mistralai/Mistral-7B-Instruct-v0.3 --gpu L40S --serverless-provider runpod --serverless-only

# Deploy on Azure with an existing environment
tuna deploy --model Qwen/Qwen3-0.6B --gpu T4 --serverless-provider azure --azure-environment my-env

# Deploy a large model with tensor parallelism
tuna deploy --model meta-llama/Llama-3-70b --gpu H100 --gpu-count 4 --tp-size 4

tuna show-gpus — Compare GPU Prices Across Providers

Show GPU pricing from all serverless providers, optionally including spot prices.

tuna show-gpus [--gpu <GPU>] [--provider <provider>] [--spot]

Examples:

# Show all GPU prices across all providers
tuna show-gpus

# Show H100 pricing specifically
tuna show-gpus --gpu H100

# Show Modal's prices only
tuna show-gpus --provider modal

# Include AWS spot prices for comparison
tuna show-gpus --spot

tuna check — Validate Provider Setup (Preflight)

Run preflight checks to verify credentials, CLIs, and quotas for a provider before deploying.

tuna check --provider <provider> [--gpu <GPU>]

Examples:

# Check Modal setup
tuna check --provider modal

# Check Azure with specific GPU
tuna check --provider azure --gpu T4 --azure-subscription <id> --azure-resource-group <rg>

tuna status — Check Deployment Status

tuna status --service-name <name>

tuna cost — Show Cost Savings Dashboard

tuna cost --service-name <name>

tuna list — List All Deployments

tuna list [--status active|destroyed|failed]

tuna destroy — Tear Down a Deployment

# Destroy a specific deployment
tuna destroy --service-name <name>

# Destroy all deployments
tuna destroy --all

Provider Setup Guide

Each serverless provider needs its own credentials. Run tuna check --provider <name> to verify setup.

Modal

pip install modal  # or: uv pip install tandemn-tuna[modal]
modal token new    # opens browser to authenticate

No environment variables needed — token is stored in Modal's config.

RunPod

export RUNPOD_API_KEY="your-api-key"

Get your API key from the RunPod console.

Google Cloud Run

pip install google-cloud-run  # or: uv pip install tandemn-tuna[cloudrun]
gcloud auth login
gcloud auth application-default login

Optionally set GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION, or pass --gcp-project and --gcp-region.

Baseten

pip install truss  # or: uv pip install tandemn-tuna[baseten]
export BASETEN_API_KEY="your-api-key"
truss login --api-key $BASETEN_API_KEY

Azure Container Apps

pip install azure-mgmt-appcontainers azure-identity  # or: uv pip install tandemn-tuna[azure]
az login
az provider register --namespace Microsoft.App
az provider register --namespace Microsoft.OperationalInsights

Pass --azure-subscription, --azure-resource-group, and --azure-region on deploy, or set AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, AZURE_REGION env vars. First deploy creates a GPU environment (~30 min); subsequent deploys reuse it (~2 min). Use --azure-environment to specify an existing environment.

Cerebrium

pip install cerebrium  # or: uv pip install tandemn-tuna[cerebrium]
cerebrium login
export CEREBRIUM_API_KEY="your-api-key"

Note: Hobby plan gives T4, A10, L4, L40S. A100 and H100 require Enterprise.

Spot GPUs (AWS via SkyPilot)

Spot is included automatically in hybrid deploys. Just configure AWS:

aws configure  # set access key, secret key, region

Use --serverless-only to skip spot if you don't have AWS set up.

Common Scenarios

When the user wants to deploy a model for quick testing: Use --serverless-only to skip spot setup. Pick a small GPU like L4 or T4. Example:

tuna deploy --model Qwen/Qwen3-0.6B --gpu L4 --serverless-only

When the user wants the cheapest deployment: First run tuna show-gpus --spot to compare serverless and spot prices. Then deploy with hybrid mode (the default) to get spot savings. The auto provider selector already picks the cheapest serverless option for the chosen GPU.

When the user wants to compare GPU prices:

tuna show-gpus
tuna show-gpus --gpu A100
tuna show-gpus --spot  # includes AWS spot prices

When the user asks "which providers support H100?" or a specific GPU:

tuna show-gpus --gpu H100

When the user wants to deploy on a specific provider: Use --serverless-provider <name>. Run tuna check --provider <name> first to verify credentials.

When the user wants to deploy a large model (70B+): Use multiple GPUs with tensor parallelism:

tuna deploy --model meta-llama/Llama-3-70b --gpu H100 --gpu-count 4 --tp-size 4

When the user wants to check if their setup is ready:

tuna check --provider modal
tuna check --provider runpod

When the user wants to see what's currently deployed:

tuna list
tuna list --status active

When the user wants to tear down everything:

tuna destroy --all

Supported GPUs

All GPU types that tuna supports across its providers:

GPU	VRAM	Architecture	Available On
T4	16 GB	Turing	Modal, RunPod, Baseten, Azure, Cerebrium, Spot
A10	24 GB	Ampere	Cerebrium
A10G	24 GB	Ampere	Modal, Baseten, Spot
A4000	16 GB	Ampere	RunPod
A5000	24 GB	Ampere	RunPod
RTX 4090	24 GB	Ada	RunPod
L4	24 GB	Ada	Modal, RunPod, Cloud Run, Baseten, Cerebrium, Spot
A40	48 GB	Ampere	RunPod
A6000	48 GB	Ampere	RunPod
L40	48 GB	Ada	RunPod
L40S	48 GB	Ada	Modal, RunPod, Cerebrium, Spot
A100 (40 GB)	40 GB	Ampere	Modal, Cerebrium, Spot
A100 (80 GB)	80 GB	Ampere	Modal, RunPod, Azure, Baseten, Cerebrium, Spot
H100	80 GB	Hopper	Modal, RunPod, Baseten, Cerebrium, Spot
H200	141 GB	Hopper	Spot
B200	192 GB	Blackwell	Modal, Baseten
RTX PRO 6000	32 GB	Blackwell	Cloud Run

Use tuna show-gpus for current pricing across all providers.

Error Handling

Preflight check fails (tuna check): The output tells you exactly what's wrong — missing CLI tool, expired credentials, unregistered provider, insufficient quota. Fix the reported issue and re-run tuna check.

Deploy fails:

1. Run tuna check --provider <provider> --gpu <gpu> to validate the environment
2. Add -v for verbose logs: tuna deploy -v ...
3. Check tuna status --service-name <name> for deployment state

Spot instance not available: Spot GPUs depend on cloud availability. If spot fails to launch, the serverless backend keeps serving — no downtime. Try a different region with --region, or use --serverless-only.

"No provider supports GPU X": Run tuna show-gpus --gpu <GPU> to see which providers offer that GPU. Not all GPUs are available on all providers.

Azure environment takes too long: First Azure deploy creates a GPU environment (~30 min). Subsequent deploys reuse it (~2 min). Use --azure-environment to specify an existing one.