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LLMs in the Cloud vs. On-Premise

LLMs in the Cloud vs. On-Premise

Evaluating the Pros and Cons

Lucas Fiévet
January 24, 2025

In partnership with

Introduction

The rapid evolution of Large Language Models (LLMs) has created a wealth of new opportunities for asset management, from generating analyst reports to automating customer service interactions and augmenting quantitative research. Yet the question remains: should you run LLMs in the cloud, on-premise, or in a hybrid fashion? Below, we’ll explore the pros and cons of each approach, the key risks, the cost-benefit side, and hardware considerations for a modern hypothetical model like Llama 3.3 70B. We’ll also discuss how these choices affect performance, latency, and scalability, especially for time-sensitive trading applications.

Cloud vs. On-Premise: Latency, Performance, and Reliability

Latency

Cloud:
Depending on the region and the LLM provider’s infrastructure, you could face network latency. For high-frequency trading or real-time decision-making, every millisecond can matter.
On-Premise:
If your trading platforms and LLM systems are co-located, you can minimize network hops and typically achieve lower overall latency.
Hybrid:
A hybrid approach might keep latency-sensitive tasks on-premise while using the cloud for more general-purpose or large-scale training tasks.

Performance and Reliability

Cloud:
You gain access to virtually unlimited compute capacity and specialized hardware (e.g., GPUs, TPUs). You can spin up or down as needed. Reliability is high, thanks to redundant data centers, but you’re also dependent on stable internet connectivity.
On-Premise:
You have direct control over system uptime, patches, and updates, so reliability is in your hands. However, you’re limited by your on-site hardware and must handle capacity planning.

Other Factors (Geo-Redundancy, Disaster Recovery)

Cloud:
Built-in solutions for disaster recovery and geo-redundancy.
On-Premise:
You’d need your own failover sites and data replication strategies.

Is Llama 3.3 Comparable to GPT-4?

Comparing open-source or future variants of Llama to proprietary models like GPT-4 can be tricky:

Model Architecture and Training: GPT-4 is rumored to have over 100B parameters with advanced training data curation. Llama 3.3 (70B) might be close in parameter size but not necessarily in training techniques or data.
Domain-Specific Performance: In some asset management tasks (like summarizing analyst reports or extracting insights from financial statements), Llama-based models, when fine-tuned, can perform similarly to GPT-4.
Context Window: GPT-4 is known for a larger context window. Llama variants may have shorter context windows unless specifically tuned.
Ecosystem: GPT-4 has a well-established API ecosystem and plugin integrations. Llama-based models offer open-source flexibility but require more in-house engineering.

Hardware Requirements to Run Llama 3.3 (70B) at 70 Tokens/s

For a 70-billion parameter model, achieving a generation rate of ~70 tokens/s typically requires:

GPU Memory:
- Each 70B-parameter model can require ~70GB or more of VRAM to run in half-precision or 8-bit quantization. In practice, you might need 4×24GB GPUs or 2×48GB GPUs (e.g., NVIDIA A100 or H100) to comfortably load the model.
- If you want more concurrency or faster token throughput, you may need additional GPUs.
GPU Compute Power:
- A single high-end GPU can often generate 10–30 tokens/s for large LLMs, so to consistently hit 70 tokens/s, you may need multiple GPUs in parallel inference or a more powerful HPC setup (e.g., multiple H100s).
CPU and RAM:
- Sufficient CPU cores (e.g., 32–64 cores) to handle data preprocessing, handle multiple inference pipelines.
- System RAM in the 128GB+ range if you handle large batch requests or do partial model offloading.
Networking and Storage:
- Fast local storage (NVMe SSDs) for model checkpoints.
- High-bandwidth interconnects (InfiniBand or 100GbE) if distributing the model across multiple nodes.

Request/Sec Limitations: On-Premise vs. Cloud

On-Premise:
Your limit is constrained by your GPU hardware and concurrency settings. You have full control but also full responsibility to provision enough infrastructure.
Cloud:
Many providers impose rate limits for their LLM APIs, though high-tier enterprise plans offer higher throughput. You can scale up with more instances, but scaling costs can rise quickly.

In time-sensitive trading scenarios with thousands of concurrent requests, you’ll need to carefully size capacity whether on-premise or in the cloud.

Cost-Benefit Analysis: When Is On-Premise Cheaper vs. Cloud?

A well-known approach to deciding between on-premise and cloud is a total cost of ownership (TCO) model. Consider factors like:

Hardware Acquisition and Depreciation:
- On-premise: High upfront CapEx (capital expenditure).
- Cloud: Pay-as-you-go OpEx (operational expenditure).
Maintenance and Support Staff:
- On-premise: You need specialized staff (DevOps, ML engineers, HPC experts).
- Cloud: The provider handles a large part of the maintenance; you still need in-house or external experts for data preparation and model integration.
Scaling Requirements:
- On-premise: Overprovisioning or under provisioning risk.
- Cloud: Elastic scaling avoids idle hardware cost but can get expensive with large workloads.
Opportunity Cost:
- On-premise: The time and cost to source GPUs, set up HPC clusters, or build data center expansions.
- Cloud: Faster time to market but watch for egress and storage fees if you store large volumes of data.

A Simple TCO Model

Let’s define:

C_hw: Cost of on-premise hardware (one-time).
C_staff: Annual cost of specialized staff.
C_facilities: Power, cooling, data center overhead (annual).
C_cloud: Monthly pay-as-you-go cost for cloud, including usage-based fees.
t_years: Number of years to depreciate or evaluate.

On-Premise 5-Year TCO

TCO_on-premise = C_hw + Σ_i=1⁵ ( C_staff + C_facilities )

Cloud 5-Year TCO

TCO_cloud = Σ_i=1⁵ ( 12 × C_cloud(i) )

(where C_cloud(i) might vary by year if usage scales up or down)

Example:

Assume you need 4 top-tier GPUs (on-prem) at a total cost of $100,000 (depreciated over 5 years).
Annual staff + facilities cost: $50,000. Over 5 years, that’s $250,000.
On-Prem TCO: $100,000 + $250,000 = $350,000 over 5 years.
Cloud: If you spend $6,000/month in usage fees, that’s $72,000/year. Over 5 years, $360,000 total.

This example suggests the on-premise route is marginally cheaper if your workload is stable and well-utilized. If, however, your needs surge unpredictably or you want to scale down significantly in certain periods, the cloud might become more cost-efficient.

Keep in mind that ingesting or transferring large amounts of data can become expensive with cloud solutions, as providers often charge egress fees when moving data outside their environment. Moreover, high-volume data pipelines require robust bandwidth, which may strain on-premise networks or incur additional costs in the cloud. In multi-cloud or hybrid scenarios, repeated data transfers between services could inflate bills even further. Planning for these hidden costs is crucial to accurately estimate overall TCO.

Key Risks: Data Privacy, Compliance, and Security

Regardless of where you deploy your LLM, the biggest risks often relate to data privacy, compliance, and security:

Regulatory and Compliance:
- Asset management firms often handle sensitive data subject to regulations (e.g., GDPR in Europe, SEC regulations in the U.S.).
- Using LLMs in the cloud means you must ensure that your cloud provider is compliant and that you maintain data sovereignty where required.
Intellectual Property (IP) Leakage:
- If the model is hosted in a multi-tenant environment, you might be concerned that proprietary data could inadvertently leak into training processes (even if that risk is typically small with robust guardrails).
Security Breaches:
- Cloud providers generally have extensive security controls, but on-premise environments give you more direct control over your data and hardware.
- A breach in an on-premise system, however, is entirely your responsibility to address.
Model Hallucination and Output Liability:
- Large models can “hallucinate” or generate spurious results. For regulated industries, misinformation can be risky.
- Ensuring proper guardrails, whether cloud-based or on-premise, is crucial.

Can Anonymized Data Help in Using Cloud Instead of On-Premise?

One key way firms handle data privacy when using cloud-based LLMs is through anonymization or pseudonymization:

Anonymized Data Processing:
By stripping sensitive details (client IDs, trade specifics, personal data), you might mitigate compliance concerns enough to confidently use a cloud service for training or inference.
Differential Privacy Techniques:
Advanced methods ensure that aggregated data cannot be traced back to individuals, satisfying regulatory demands.
Zero-Knowledge Proofs / Encrypted ML:
Emerging techniques allow computations on encrypted data, but they can be more computationally expensive and complex to set up.

When properly anonymized, the risk of exposing sensitive or proprietary information to cloud-based providers diminishes significantly. However, extra steps are needed to ensure the anonymization pipeline is robust and meets all compliance standards.

Hybrid Approach: Best of Both Worlds?

A hybrid approach is increasingly popular for asset management:

On-Premise for Latency-Critical Tasks:
- Real-time risk assessment or trade decision-making modules that can’t afford network overhead.
Cloud for Large-Scale Training & Experimentation:
- Offload training or major batch inference tasks to the cloud for flexible scaling.
Anonymized or Aggregated Data:
- Send anonymized data to the cloud to comply with regulations while using your internal data in a more private environment.

This approach balances compliance, cost efficiency, and performance. However, it also increases complexity in data orchestration and environment management.

Final Thoughts and Next Steps

Choosing the right deployment strategy for LLMs in asset management depends on use-case requirements:

Privacy: On-premise or anonymized/hybrid solutions might be preferable for regulated data.
Latency: If real-time or near-real-time is critical, on-premise can minimize latency.
Scaling and Cost: If your usage is sporadic or you lack upfront capital, the cloud’s pay-as-you-go model might save money.
Long-Term TCO: If you plan for consistent, heavy usage, on-premise could be more cost-effective in the long run.