name	dell-ai
description	Skill for interacting with Dell Enterprise Hub (DEH) - discovering models, exploring hardware platforms, generating deployment snippets, etc.

Dell AI

dell-ai interacts with the Dell Enterprise Hub (DEH) — discovering models, exploring hardware platforms, generating deployment snippets, managing app catalog entries, checking system compatibility, etc.

When to use

User wants to find AI models available on Dell Enterprise Hub
User wants to find AI models available on DEH for their hardware platform
User needs to check which Dell hardware platforms support a given model
User wants to generate Docker or Kubernetes deployment commands for a model and then run it.
User wants a goodput-optimized deployment snippet (balanced, long-context, high-concurrency, performance).
User wants to explore or deploy applications from the Dell app catalog
User needs to check system compatibility with Dell AI platforms
User asks about Dell AI, Dell Enterprise Hub, or DEH
User wants to authenticate with Dell AI using a Hugging Face token
User wants to check their system info or check system compatibility against Dell AI validated configs

Installation

uv pip install dell-ai

Authentication

Uses a Hugging Face token. Auto-loads from HF token cache, or accepts one directly.

from dell_ai import DellAIClient

client = DellAIClient()                        # Uses cached HF token
client = DellAIClient(token="your_hf_token")   # Or explicit token

client.is_authenticated()  # Returns bool
client.get_user_info()     # Returns dict with name, email, orgs, etc.

Models

Search and list models

list_models returns List[str] of IDs. search_models accepts the same filters but returns full Model objects.

# List model IDs (all filters are optional and combinable)
models = client.list_models(
    query="llama",                    # Search name/description
    multimodal=True,                  # True for multimodal, False for text-only
    min_size=7000,                    # Min params in millions
    max_size=70000,                   # Max params in millions
    license_filter="apache",          # License substring match
    platform_id="xe9680-nvidia-h200", # Only models compatible with this platform
)

# Full Model objects with same filters
results = client.search_models(query="llama", multimodal=True)
# Model fields: repo_name, description, license, size, is_multimodal,
#               has_system_prompt, creator_type, status, configs_deploy

Get model details and compatible platforms

# Returns Model object
model = client.get_model("meta-llama/Llama-4-Maverick-17B-128E-Instruct")

platforms = client.get_compatible_platforms("google/gemma-3-27b-it")
# Returns List[PlatformCompatibility] with platform_id and configs (List[ModelConfig])
for p in platforms:
    print(p.platform_id, [c.model_dump() for c in p.configs])

Check model access (gated repos)

# Returns True, or raises GatedRepoAccessError / AuthenticationError
client.check_model_access("meta-llama/Llama-4-Maverick-17B-128E-Instruct")

Get model Deployment Snippets

Automatically validates model access and model-platform compatibility.

Provide either num_gpus (manual sizing) or goodput (server-optimized) — the two are mutually exclusive, and exactly one is required.

# Basic deployment snippet with manual GPU sizing
snippet = client.get_deployment_snippet(
    model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct",
    platform_id="xe9680-nvidia-h200",
    engine="docker",       # "docker" or "kubernetes"
    num_gpus=8,
    num_replicas=1,
)
print(snippet)  # Ready-to-use docker command or k8s manifest

# Goodput-optimized: DEH returns the GPU count and optimized params for the
# scenario. Omit num_gpus when using goodput.
snippet = client.get_deployment_snippet(
    model_id="nvidia/MiniMax-M2.7-NVFP4",
    platform_id="xe9680-nvidia-h200",
    engine="docker",
    goodput="balanced",    # balanced | long-context | high-concurrency | performance
)

Not every (model, platform, scenario) combination has an optimized config; the API is the source of truth and raises ResourceNotFoundError with a message like No optimized config for "balanced" scenario on this SKU. when it doesn't.

Goodput scenarios reference data

Global, static reference data: scenario definitions, SLO field docs, and SLO targets per SKU. Cached on disk after the first call.

ref = client.get_goodput_scenarios()
# GoodputReference fields:
#   scenarios            -> List[Scenario] (id, label, description)
#   slo_field_descriptions -> Dict[str, str]
#   slos_by_sku          -> Dict[SkuId, Dict[scenario, Slo]] (sparse)
# Slo fields: max_model_context, virtual_users, input_tokens [min,max], output_tokens [min,max]

slo = ref.slos_by_sku.get("xe9680-nvidia-h100", {}).get("balanced")

Platforms

platforms = client.list_platforms()  # Returns List[str] of SKU IDs

platform = client.get_platform("xe9680-nvidia-h200")
# Fields: id, name, platform_type, vendor, accelerator_type, accelerator,
#   gpuram, gpuinterconnect, totalgpucount, product_name

sys_infos = client.get_platform_system_info("xe9680-nvidia-h200")  # List[SystemInfo]

Applications

List and inspect apps

apps = client.list_apps()  # Returns List[str] of app names

app = client.get_app("openwebui")
# App fields: id, name, license, description, features, instructions,
#   tags, recommendedModels, components

Explore configurable parameters

for component in app.components:
    for param in component.config:
        print(f"  {param.name}: {param.helmPath} ({param.type}), default={param.default}")
    for secret in component.secrets:
        print(f"  [secret] {secret.name}: {secret.helmPath}")

Generate app deployment snippet

Each config item requires: helmPath, type ("string", "boolean", "number", or "json"), and value.

config = [
    {"helmPath": "main.config.storageClassName", "type": "string", "value": "gp2"},
    {"helmPath": "main.config.enableOpenAI", "type": "boolean", "value": True},
]
snippet = client.get_app_snippet("openwebui", config)
print(snippet)  # Helm install command

System Utilities (CLI only, Linux)

dell-ai utils describe-system              # Get system info as JSON
dell-ai utils describe-system -o out.json  # Save to file
dell-ai utils describe-system | jq         # Pretty-print
dell-ai utils check-system                 # Check compatibility against Dell AI validated configs

Requires: lscpu, lspci, lsblk, dmidecode. For GPUs: nvidia-smi, nvidia-ctk. For K8s: kubectl.

Exceptions

All in dell_ai.exceptions:

AuthenticationError — Invalid or missing token
GatedRepoAccessError — No access to a gated model repository
ResourceNotFoundError — Model, platform, or app not found
ValidationError — Invalid parameters (may include valid_values)
APIError — General API errors

name	dell-ai
description	Skill for interacting with Dell Enterprise Hub (DEH) - discovering models, exploring hardware platforms, generating deployment snippets, etc.