ZTABS deploys self-hosted AI systems for Los Angeles organizations that cannot send data to external AI providers — healthcare systems processing protected health information under HIPAA where patient records, clinical notes, medical images, and genomic data must remain within the institution's infrastructure and cannot be transmitted to OpenAI, Anthropic, or any third-party AI service regardless of their security certifications because healthcare leadership and legal counsel have determined that the risk of patient data exposure through third-party AI processing is unacceptable given the $50,000+ per-violation HIPAA penalty structure and the reputational destruction that a PHI breach would cause to institutions like UCLA Health, Cedars-Sinai, and Kaiser Permanente Southern California, defense contractors handling classified and controlled unclassified information that is prohibited from leaving accredited facilities under ITAR and NIST 800-171 requirements where even the metadata of AI queries could reveal sensitive program information, and entertainment studios protecting unreleased content — scripts, dailies, VFX renders, and strategic development plans — worth hundreds of millions in IP value that would be catastrophically damaged by premature exposure.
Self-hosted AI deployment in Los Angeles serves organizations where data sovereignty is a non-negotiable requirement. Why self-hosted AI in LA: cloud AI services (OpenAI API, Anthropic API, Google Vertex AI, AWS Bedrock) offer powerful AI capabilities with minimal infrastructure overhead. For many organizations, cloud AI is the right choice. But for LA's most sensitive industries — healthcare, defense, entertainment, and financial services — cloud AI creates unacceptable data exposure risk. The data that makes AI valuable in these industries is exactly the data that cannot leave the organization's control. Healthcare: the AI use cases with the highest value in healthcare — clinical decision support, medical image analysis, clinical note processing, genomic analysis, and drug interaction prediction — all require access to protected health information (PHI). Under HIPAA, organizations can share PHI with business associates under a BAA (Business Associate Agreement), and major cloud AI providers offer BAAs. But many healthcare organizations have concluded that a BAA is insufficient — a BAA transfers legal liability but doesn't prevent a breach. A breach of PHI sent to a cloud AI provider would still result in patient notification, OCR investigation, potential penalties, class action litigation, and reputational damage. The risk calculus for many LA health systems: the liability exposure from sending millions of patient records to external AI services outweighs the infrastructure cost of running AI on-premises. Defense: the calculation is simpler — classified information cannot be processed on commercial cloud services (with narrow exceptions for specific FedRAMP High and IL4/IL5 environments). Controlled Unclassified Information (CUI) can theoretically be processed in compliant cloud environments, but many defense programs restrict CUI processing to contractor-controlled infrastructure as an additional security measure. When a defense contractor's AI system processes technical documents, engineering queries, or program data, even the queries themselves may reveal sensitive information about program capabilities, schedule, or challenges. Self-hosted AI ensures that no program information — not data, not queries, not AI outputs — leaves the contractor's secure network. Entertainment: LA's entertainment studios protect unreleased content with extraordinary rigor. Scripts for major productions are physically watermarked, digitally tracked, and distributed under strict NDAs. Pre-release footage (dailies, rough cuts, VFX renders) is stored in secure facilities with limited access. Development plans (what properties are being developed, who is attached, what budgets are approved) are considered highly sensitive competitive intelligence. AI systems that process this content — script analysis, content recommendation, audience prediction, VFX processing — must operate within the studio's infrastructure. A leaked script or unreleased footage would cause millions in damage and would end the studio's willingness to use AI tools entirely. Financial services: LA's financial institutions process customer financial data that is subject to Gramm-Leach-Bliley Act (GLBA) protections, CCPA/CPRA privacy requirements, and fiduciary obligations. AI systems that analyze customer portfolios, assess credit risk, or detect fraud process exactly the data that regulators expect to be protected most carefully. Self-hosted AI allows financial institutions to leverage AI capabilities while maintaining complete control over customer data.
Los Angeles self-hosted AI demand spans healthcare, defense, entertainment, and financial services. Healthcare Self-Hosted AI: clinical NLP (self-hosted natural language processing for clinical documentation — processing physician notes, radiology reports, pathology reports, and discharge summaries to extract diagnoses, procedures, medications, and outcomes — for LA healthcare: clinical NLP processes millions of notes annually across LA's major health systems — UCLA Health alone generates an estimated 15,000+ clinical notes per day — self-hosted deployment ensures that no patient information leaves the health system's network — the NLP models run on the health system's own GPU infrastructure, processing notes within the secure clinical data environment), medical imaging AI (self-hosted AI for medical image analysis — radiology (CT, MRI, X-ray), pathology (digital pathology slides), dermatology (skin lesion classification), and ophthalmology (retinal imaging) — for LA healthcare: medical images are both PHI (protected under HIPAA) and large files (a single CT study can be 500MB+) — sending images to cloud AI for analysis would create both privacy risk and bandwidth challenges — self-hosted deployment processes images within the health system's PACS (Picture Archiving and Communication System) infrastructure — AI results are stored alongside images in the clinical record), and genomic analysis (self-hosted AI for genomic data processing — variant calling, pathogenicity prediction, pharmacogenomic analysis, and tumor profiling — for LA healthcare: genomic data is among the most sensitive health information — it is permanently identifying (you can't change your genome like you can change a password), reveals information about family members (not just the patient), and has implications for insurance and employment (despite GINA protections) — LA's academic medical centers (UCLA, USC, Cedars-Sinai) conduct extensive genomic research — self-hosted AI ensures that genomic data remains within institutional control)). Defense Self-Hosted AI: classified AI processing (self-hosted AI for classified environments — running AI models within SCIFs (Sensitive Compartmented Information Facilities) or on classified networks that are physically isolated from the internet — for LA defense: classified AI requires specialized infrastructure — GPU servers that meet TEMPEST standards (preventing electromagnetic emissions that could be intercepted), air-gapped networks with no internet connectivity, and software that has been evaluated and approved for classified use — we deploy self-hosted AI models that operate entirely within classified infrastructure, requiring no external connectivity), technical document AI (self-hosted AI for processing technical documentation — specifications, test reports, engineering analyses, and program data — using RAG systems and specialized AI tools that run within the contractor's secure network — for LA defense: even unclassified technical documents may contain CUI (Controlled Unclassified Information) that requires protection under NIST 800-171 — self-hosted deployment ensures that CUI is processed only within compliant infrastructure and that no technical data is exposed to external AI providers), and supply chain AI (self-hosted AI for defense supply chain management — supplier evaluation, risk assessment, demand forecasting, and logistics optimization — processing supply chain data that may reveal program schedules, quantities, and capabilities — for LA defense: supply chain data aggregation can reveal classified information even when individual data points are unclassified — the combination of what is being ordered, from whom, in what quantities, and on what schedule can reveal program characteristics — self-hosted AI ensures that supply chain analysis occurs within the contractor's secure environment)). Entertainment Self-Hosted AI: content analysis AI (self-hosted AI for analyzing unreleased content — script analysis (evaluating narrative quality, audience appeal, production complexity), dailies review (AI-assisted review of daily footage for continuity, quality, and performance), VFX processing (AI-enhanced visual effects processing that operates within the studio's secure render farm), and content recommendation (analyzing the studio's complete IP library to identify development opportunities) — for LA studios: content is the studio's most valuable asset — an unreleased script for a $200M production or pre-release footage from a highly anticipated sequel has enormous value that would be destroyed by premature exposure — self-hosted AI processes this content within the studio's secure infrastructure), and audience intelligence (self-hosted AI for audience analysis — processing proprietary audience research data, subscriber behavior data, and internal market analysis to predict audience demand and inform greenlighting decisions — for LA streaming: subscriber data is competitively sensitive — which titles subscribers watch, when they cancel, what content drives acquisition — this data is the basis for programming decisions worth billions — self-hosted AI ensures that competitive intelligence remains internal)). Financial Services Self-Hosted AI: risk and compliance AI (self-hosted AI for financial risk assessment and compliance — credit risk modeling, fraud detection, anti-money laundering (AML) analysis, and regulatory compliance monitoring — processing customer financial data that is subject to GLBA, CCPA/CPRA, and fiduciary protections — for LA financial: self-hosted deployment ensures that customer financial data is processed within the institution's infrastructure, maintaining regulatory compliance and customer trust), and portfolio analytics (self-hosted AI for investment analysis — portfolio optimization, market prediction, and client recommendation — processing proprietary trading strategies and client portfolio data — for LA financial: investment strategies and client portfolios are highly confidential — AI that processes this data in a self-hosted environment eliminates the risk of competitive intelligence exposure through third-party AI providers)).
Our self-hosted AI deployment methodology for Los Angeles provides full AI capability within the organization's infrastructure perimeter. Phase 1 — Infrastructure Assessment (1-2 weeks): evaluating the organization's readiness for self-hosted AI: compute inventory (assessing existing compute infrastructure — available GPU resources (type, quantity, VRAM), CPU capacity, storage capacity and speed, and network bandwidth — for healthcare: many hospitals have limited GPU infrastructure — historically, GPU workloads (medical imaging, genomic analysis) have been handled by specialized appliances rather than general-purpose GPU servers — self-hosted AI may require GPU infrastructure investment), security environment assessment (evaluating the existing security infrastructure — network architecture (segmentation, air gaps, DMZ), identity management (Active Directory, CAC, MFA), encryption (at rest, in transit), audit logging, and compliance certifications (HIPAA, FedRAMP, CMMC, SOC 2) — for defense: the assessment determines which classified environment the AI will operate in — Unclassified, CUI, Secret, or Top Secret — each with different infrastructure requirements), model requirements analysis (determining which AI models the organization needs — LLM for text processing (what size model is required? Smaller models run on fewer GPUs but have lower capability. Larger models require more infrastructure but handle more complex tasks), computer vision models for image analysis, and specialized models for domain-specific tasks — model requirements drive hardware specifications — a 70B parameter LLM requires 4x A100 80GB GPUs or equivalent for inference), and cost-benefit analysis (comparing self-hosted costs (infrastructure, operations, energy) against cloud AI costs — self-hosted AI has higher upfront costs but lower per-query costs at scale — for organizations processing millions of AI queries annually, self-hosted is often less expensive than cloud API pricing — the cost-benefit analysis determines whether self-hosted is economically justified in addition to being security-required)). Phase 2 — Infrastructure Deployment (2-4 weeks): building the AI compute infrastructure: hardware procurement and installation (specifying and deploying GPU servers — for most LA deployments: NVIDIA A100 or H100 GPU servers with high-bandwidth NVLink interconnects — server configuration depends on model size and throughput requirements — for healthcare: GPU servers are typically installed in the health system's existing data center, connected to the clinical data environment — for defense: GPU servers may require TEMPEST-certified enclosures and installation in SCIFs — for entertainment: GPU servers integrate with existing render farm infrastructure), model deployment (deploying selected AI models on the infrastructure — LLM deployment using inference frameworks (vLLM, TGI, Ollama) that optimize GPU utilization and support concurrent users — model quantization (reducing model precision from FP16 to INT8 or INT4) when GPU resources are limited — trading marginal quality reduction for 2-4x throughput improvement — for healthcare: deploying medical-specific models (clinical NLP, medical imaging) alongside general-purpose LLMs — for defense: deploying models evaluated and approved for the target classification environment), and API layer (building an API gateway that provides a consistent interface for applications to access self-hosted AI — the API layer provides authentication, rate limiting, usage tracking, and audit logging — the API mimics the interface of cloud AI providers (OpenAI-compatible API) so that applications can switch between cloud and self-hosted AI without code changes — for defense: the API layer enforces classification controls, ensuring that queries and responses are handled at the appropriate classification level)). Phase 3 — Application Integration (3-6 weeks): connecting self-hosted AI to business applications: EHR integration (for healthcare: integrating self-hosted AI with the health system's electronic health record — Epic, Cerner, or other EHR systems — through native integration points (Epic's CDS Hooks for clinical decision support, SMART on FHIR for EHR-embedded applications) — AI capabilities appear within the clinician's existing workflow — clinical NLP processes notes as they are documented, medical imaging AI provides analysis within the PACS viewer, and decision support recommendations appear in the EHR — the clinician doesn't interact with a separate AI system — AI is embedded in their existing tools), engineering tool integration (for defense: integrating self-hosted AI with engineering tools — CAD systems (CATIA, NX, Solidworks), PLM systems (Teamcenter, Windchill), and document management systems — engineers access AI capabilities from within their existing tools — a query from within the PLM system retrieves relevant technical documents without the engineer leaving the PLM environment), content workflow integration (for entertainment: integrating self-hosted AI with content management systems, digital asset management, and production tools — script analysis integrates with development tracking systems, VFX AI integrates with render pipeline tools, and content recommendation integrates with programming dashboards), and custom application development (building custom applications that leverage self-hosted AI for specific use cases — dashboards, analysis tools, and workflow applications that connect to the self-hosted AI API — for all LA deployments: custom applications provide the user experience layer that makes AI capabilities accessible to non-technical users)). Phase 4 — Operations and Optimization (ongoing): maintaining and improving self-hosted AI: monitoring and performance management (monitoring GPU utilization, inference latency, throughput, and error rates — optimizing model performance through batch processing configuration, caching strategies, and load balancing across multiple GPU servers — for healthcare: monitoring includes clinical safety metrics — tracking AI recommendation accuracy and flagging degradation that could affect patient care), model updates (updating models as new versions become available — self-hosted models don't automatically update like cloud services — we implement a model evaluation pipeline that tests new model versions against the organization's specific use cases before deployment — for defense: model updates require security review and approval before deployment in classified environments), capacity planning (projecting future AI compute needs based on usage trends — planning infrastructure expansion before demand exceeds capacity — for healthcare: capacity planning accounts for new AI use cases (each new clinical AI application adds GPU demand) and organizational growth — for entertainment: capacity planning accounts for production cycles — VFX processing demand spikes during post-production periods for major releases), and cost optimization (optimizing the cost of self-hosted AI operations — energy costs (GPU servers consume significant power — 5-10 kW per server), cooling costs, and maintenance costs — strategies include workload scheduling (running batch processing during off-peak hours when energy costs are lower), model optimization (using smaller, more efficient models for simpler tasks), and infrastructure sharing (allowing multiple departments to share GPU infrastructure through resource scheduling))).
Healthcare / HIPAA-Compliant Medical Research
A large healthcare system in Los Angeles — operating 6 hospitals, 42 outpatient clinics, and a medical research institute across LA County, with 2.1 million patient encounters annually and 18,000 employees — wanted to deploy AI across clinical and research operations but faced a fundamental obstacle: its legal, compliance, and IT security teams unanimously determined that sending patient data to external AI providers was unacceptable under any circumstances. The prohibition was absolute: no patient data — clinical notes, lab results, imaging studies, genomic data, or any data derived from patient records — could be transmitted to OpenAI, Google, Anthropic, Microsoft Azure AI, AWS Bedrock, or any other external AI service. The reasoning: HIPAA risk exposure (the health system processed 8.4 million clinical notes annually, 2.3 million imaging studies, and 45,000 genomic analyses — sending even a fraction of this data to external AI would create massive PHI exposure — a breach at the AI provider would affect millions of patients and expose the health system to penalties potentially exceeding $100M), research data sensitivity (the research institute conducted 280 active clinical trials with data from 34,000 enrolled participants — trial data was subject to IRB (Institutional Review Board) protocols that prohibited data sharing with entities not covered by the research protocol — sending trial data to external AI would violate IRB protocols and could result in trial suspension), competitive intelligence (the health system's data — patient volumes, service line performance, payer mix, outcomes data — was competitively sensitive — sending this data to external AI providers (who also served competing health systems) created a perceived risk of competitive intelligence exposure), and liability precedent (legal counsel determined that voluntarily sending PHI to external AI providers — even under a BAA — could be viewed as insufficient data protection in the event of a breach — "We chose to send patient data to an external service when we could have processed it internally" was a liability argument the health system wanted to avoid). The result: the health system's clinicians and researchers were aware of AI capabilities but had no access to them. Clinicians used manual processes for clinical documentation, diagnostic support, and care coordination. Researchers manually reviewed literature, analyzed data, and wrote reports. The health system estimated that it was missing $8-12M annually in operational efficiencies and research productivity that AI could provide — but the data security requirements prevented adoption.
We designed and deployed a comprehensive self-hosted AI platform over 14 weeks — providing the full range of AI capabilities within the health system's infrastructure. Infrastructure: we deployed a dedicated AI compute cluster within the health system's HIPAA-compliant data center. Hardware: 8 GPU servers (each with 4x NVIDIA A100 80GB GPUs) for AI inference, plus 2 GPU servers for model training and fine-tuning. Total GPU: 40 A100 GPUs providing 3.2TB of aggregate GPU memory. The cluster was connected to the health system's clinical data warehouse through a secure internal network — no external connectivity. All data remained within the health system's infrastructure perimeter. Storage: 200TB of high-speed NVMe storage for model weights, embeddings, and processing cache, connected to the health system's existing 5PB clinical data warehouse. Model deployment: we deployed multiple AI models optimized for different use cases. General-purpose LLM: Llama 3.1 70B (open-source, no licensing restrictions on healthcare use) — deployed using vLLM inference server with INT8 quantization — running across 8 A100 GPUs — providing a throughput of 180 queries per minute with average latency of 2.8 seconds. Clinical NLP model: a fine-tuned model specifically trained on clinical documentation — extracting diagnoses (ICD-10), procedures (CPT), medications (RxNorm), and clinical findings from unstructured physician notes with 94% accuracy. Medical imaging model: specialized computer vision models for radiology (chest X-ray, CT pulmonary angiogram) deployed on dedicated GPUs — processing images from the health system's PACS and returning analysis results within 15 seconds. Biomedical research model: BioMedLM (a model pre-trained on biomedical literature) fine-tuned on the health system's research publications and clinical trial protocols — supporting literature review, protocol analysis, and research synthesis. API gateway: a central API gateway provided authenticated access to all AI capabilities — clinicians and researchers accessed AI through integrated applications (not directly through APIs). The API gateway enforced role-based access control (clinicians accessed clinical AI tools, researchers accessed research AI tools), logged all interactions for HIPAA audit compliance, and rate-limited usage to ensure equitable access across departments. Clinical Applications: clinical documentation assistant — integrated with the health system's Epic EHR through Epic's SDK. Capabilities: note summarization (summarizing lengthy patient histories for hand-off and care coordination), diagnostic suggestion (analyzing documented symptoms, lab results, and imaging findings to suggest differential diagnoses — presented as decision support, not diagnostic determination), medication interaction checking (enhanced drug interaction analysis using the full context of the patient's medical history — going beyond standard drug-drug interaction databases to consider patient-specific factors), and discharge planning (generating draft discharge instructions based on the patient's hospital course, medications, and follow-up needs — reviewed and approved by the discharging physician). Research Applications: literature review assistant — a RAG system that ingested the health system's internal publication database (18,000 publications) and indexed summaries of 35 million PubMed articles — enabling researchers to query across internal and external literature. Protocol analysis — AI-assisted review of clinical trial protocols for completeness, consistency, and regulatory compliance. Data analysis — AI-assisted analysis of research datasets — generating statistical summaries, identifying patterns, and suggesting additional analyses based on the data structure. Operational Applications: coding optimization — AI-assisted medical coding that analyzed clinical documentation and suggested appropriate ICD-10 and CPT codes — improving coding accuracy and reducing revenue cycle delays. Capacity planning — AI analysis of patient flow data to predict bed demand, staffing needs, and resource requirements 24-72 hours in advance.
The platform was deployed in phases — clinical documentation (month 1), research tools (month 2), and operational applications (month 3). Adoption: clinical users: 2,400 clinicians (physicians, NPs, PAs) used the clinical documentation assistant within 6 months — representing 68% of the clinical staff. Average usage: 8.4 AI interactions per clinician per day. Research users: 340 researchers used the literature review and data analysis tools — representing 82% of the research institute's active investigators. Operational users: 85 coding specialists and 24 capacity planning staff used the operational applications. Clinical Impact: documentation time: clinicians reported saving an average of 42 minutes per day through AI-assisted documentation — note summarization, diagnostic suggestion review, and discharge instruction generation reduced the time spent on documentation (the primary complaint of clinicians nationwide). Across 2,400 clinicians: 1,680 hours saved per day — equivalent to 210 additional clinician FTEs in patient-facing capacity. Coding accuracy: AI-assisted coding improved first-pass coding accuracy from 78% to 91% — reducing coding denials and rework. The revenue cycle team estimated that improved coding accuracy recovered $3.2M in annual revenue that was previously lost to under-coding and coding errors. Diagnostic support: the diagnostic suggestion feature was used in 45,000 clinical encounters per month. In a quality review of 500 randomly selected encounters, the AI's diagnostic suggestions included the ultimately confirmed diagnosis in 89% of cases. In 12% of reviewed cases, the AI suggested a diagnosis that the clinician had not initially considered and that proved relevant — expanding the differential diagnosis and potentially improving diagnostic accuracy. Research Impact: literature review time: researchers reported that AI-assisted literature review reduced the time to complete a comprehensive literature review from an average of 40 hours to 12 hours — a 70% reduction. Across 340 researchers conducting an average of 4 literature reviews per year: 38,080 researcher-hours saved annually. Grant application support: AI-assisted protocol analysis and literature synthesis supported 45 grant applications in the first year — of which 22 were funded (a 49% success rate vs. the institution's historical 31% rate — while causation cannot be definitively attributed to AI, researchers reported that AI-assisted applications were more thoroughly researched and better cited). Operational Impact: capacity prediction: the AI-based capacity planning system predicted next-day bed demand with 87% accuracy (within ±5 beds) — enabling proactive staffing adjustments that reduced overtime costs and improved patient flow. The estimated annual impact of improved capacity planning: $1.8M in reduced overtime and improved bed utilization. Financial Summary (Year 1): documentation time recovery: 1,680 hours/day × 250 work days × value varies by clinician type (estimated blended value: $150/hour) = $63M in recovered clinical capacity (not all converted to revenue — but significant improvement in clinician workload and patient access). Coding revenue recovery: $3.2M. Research productivity: 38,080 hours × $95/hour (researcher cost) = $3.6M in recovered research capacity. Capacity planning: $1.8M. Total quantifiable impact: $71.6M (with the documentation time recovery being primarily capacity and quality improvement rather than direct revenue). Infrastructure cost: $3.8M (hardware, deployment, integration). Annual operating cost: $720,000 (energy, maintenance, operations staff). The platform's infrastructure cost was recovered within the first 3 months through coding revenue improvement and operational efficiency alone — the clinical and research productivity improvements represented additional ongoing value.
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Model quantization (GPTQ, AWQ, GGUF) and inference optimization to maximize performance on your hardware.
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Los Angeles self-hosted AI providers include cloud platform alternatives, healthcare IT companies, and defense IT contractors. (1) Cloud platforms with on-premises options (Azure Stack, AWS Outposts, Google Distributed Cloud) — provide some on-premises AI capability but still rely on cloud-connected infrastructure and vendor-managed models that may not satisfy the most stringent data sovereignty requirements. (2) Healthcare IT companies — understand HIPAA but often lack the GPU infrastructure and model optimization expertise for high-performance self-hosted AI. (3) Defense IT contractors — understand classified environments but may lack the healthcare and entertainment domain expertise. ZTABS deploys fully self-hosted AI systems with zero external data exposure — optimized for the specific performance, security, and domain requirements of LA's most sensitive industries.
Self-hosted AI deployment in Los Angeles varies by infrastructure scale, security requirements, and integration complexity. Pilot Deployment (single use case, limited scale, 4-6 weeks): $150,000-$300,000 + hardware costs. Production Deployment (multiple use cases, enterprise integration, 8-14 weeks): $400,000-$800,000 + hardware costs. Enterprise AI Platform (organization-wide, multiple models, full integration, 14-24 weeks): $800,000-$1,500,000 + hardware costs. Hardware: $400,000-$2,000,000+ depending on GPU count and security requirements. Ongoing: $15,000-$40,000/month for operations, monitoring, and model updates. ZTABS delivers at $250-350/hr — AI infrastructure engineers with healthcare HIPAA, defense classified, and entertainment content security expertise.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract. We ship and operate our own software. 23 products in production.
Common questions about self-hosted AI & private LLM deployment for Los Angeles businesses
Hardware requirements depend on the AI models deployed. For LLM inference (a 70B parameter model like Llama 3.1 70B): minimum 4x A100 80GB GPUs for INT8 quantized inference, or 8x A100s for full-precision with higher throughput. For medical imaging AI: 1-2 A100 GPUs per model depending on model complexity. For a healthcare system running multiple AI applications, we typically recommend 8-12 GPU servers (32-48 A100/H100 GPUs). Costs range from $400K-$2M for GPU infrastructure. The investment is justified when query volume exceeds approximately 100,000 queries per month — at that volume, self-hosted per-query costs are lower than cloud API pricing.
ZTABS is a remote-first self-hosted AI & private LLM deployment agency serving Houston businesses — including private llm deployment, openclaw setup & management, gpu infrastructure provisioning. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Self-Hosted AI & Private LLM Deployment in New York, NYZTABS is a remote-first self-hosted AI & private LLM deployment agency serving New York businesses — including private llm deployment, openclaw setup & management, gpu infrastructure provisioning. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Self-Hosted AI & Private LLM Deployment in Chicago, ILZTABS is a remote-first self-hosted AI & private LLM deployment agency serving Chicago businesses — including private llm deployment, openclaw setup & management, gpu infrastructure provisioning. We work with Finance & Trading, Manufacturing, Transportation & Logistics companies in Chicago, IL via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Development in Los Angeles, CAZTABS is a remote-first web development agency serving Los Angeles businesses — including full-stack development, progressive web apps, api development. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Design in Los Angeles, CAZTABS is a remote-first web design agency serving Los Angeles businesses — including ui/ux design, responsive design, custom interfaces. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in Los Angeles, CAZTABS is a remote-first AI development agency serving Los Angeles businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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