Deploy Dedicated GPU server to run AI models

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LLM

Phi-4 API on dedicated GPU

Phi-4 is a strong fit for smaller dedicated enterprise deployments where teams want a compact model footprint without leaving shared hosted services in the loop.

Inputs

Prompts, internal app context, private business workflows

Outputs

Compact open LLM responses on dedicated infrastructure

Phi-4 sample output

Why teams deploy Phi-4

Dedicated enterprise hosting is useful for Phi-4 when the workload includes sensitive prompts, proprietary assets, internal product context, or runtime customization that does not belong on a shared public endpoint.

compact private LLM hosting
cost-aware enterprise assistants
smaller inference footprints

Deployment profile

Modality
LLM
Deployment
Dedicated compact LLM runtime on enterprise GPU
Pricing floor
$1999/month

What you can run

Chat
Private inference
Dedicated runtime control
Enterprise deployment

Common enterprise use cases

internal helpers
automation backends
private app copilots

Why ModelsLab Enterprise fits this model

Dedicated GPU deployment with no shared queue contention
100% private workloads, prompts, and generated outputs
Code access for custom runtimes, adapters, and optimization
Bring-your-own S3 storage for assets, checkpoints, and outputs
Enterprise Deployment

Deploy this model on dedicated GPU

Deploy Phi-4 with dedicated GPUs, private data flow, code access, and S3-backed storage so your team can run production workloads without shared infrastructure tradeoffs.

100% privacy for prompts, inputs, and outputs
Code access for custom runtimes and adapters
Bring-your-own S3 for checkpoints and generated assets
Dedicated GPU throughput with no shared queue

Pricing

$1999/month

Starting price for enterprise dedicated GPU plans. Move to higher GPU tiers when you need more VRAM, throughput, or concurrency.

Related enterprise model pages

Use these related pages to compare adjacent models in the same deployment category.

DeepSeek R1 sample output
LLMDedicated GPU

DeepSeek R1

DeepSeek R1 is one of the clearest enterprise deployment wins in the open LLM landscape because teams want its reasoning ability without exposing prompts or internal context to third-party shared providers.

Chat completionsPrivate prompt handling
DeepSeek V3 sample output
LLMDedicated GPU

DeepSeek V3

DeepSeek V3 is a strong dedicated enterprise target when teams want a cost-aware open LLM stack for private production inference.

Chat completionsPrivate prompt flow
DeepSeek Coder V2 sample output
LLMDedicated GPU

DeepSeek Coder V2

DeepSeek Coder V2 is a natural fit for private engineering copilots where source code and developer prompts should stay inside dedicated infrastructure.

Coding chatPrivate code context
Llama 3.3 70B sample output
LLMDedicated GPU

Llama 3.3 70B

Llama 3.3 70B remains a high-intent enterprise model page because teams actively compare private open-weight Llama deployments against shared hosted APIs.

Chat completionsPrivate context handling
Llama 3.1 8B sample output
LLMDedicated GPU

Llama 3.1 8B

Llama 3.1 8B is attractive for teams that want a smaller dedicated LLM footprint while keeping prompts, retrieval context, and code-level runtime changes private.

ChatPrivate inference
Qwen 3 32B sample output
LLMDedicated GPU

Qwen 3 32B

Qwen 3 32B is a strong open LLM candidate for private multilingual and reasoning workloads that need enterprise-grade control instead of shared hosted endpoints.

Chat completionsPrivate prompt flow

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