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Self-hosting LLMs requires expertise across model architecture, inference optimization, infrastructure engineering, and operational security. EaseCloud has deployed self-hosted LLMs across healthcare, finance, legal, and enterprise SaaS environments where data privacy and performance are non-negotiable.
We benchmark open-source LLMs including Llama 3, Mistral, Mixtral, Phi-3, and Falcon against your specific tasks, selecting the model that delivers target accuracy at the lowest hardware cost.
We apply GGUF, GPTQ, and AWQ quantization strategies that reduce model memory footprint by 50–75%, enabling deployment on fewer GPUs while preserving 95%+ of full-precision accuracy.
We deploy and configure vLLM, Text Generation Inference, and NVIDIA Triton with optimized KV cache settings, continuous batching, and speculative decoding that maximize throughput per GPU.
We implement Kubernetes-based auto-scaling that dynamically provisions inference replicas based on request queue depth, maintaining SLA-compliant response times under variable load.
We build supervised fine-tuning and PEFT (LoRA, QLoRA) pipelines that adapt foundation models to your domain, improving task-specific accuracy by 15–40% compared to zero-shot prompting.
EaseCloud delivers end-to-end LLM deployment services: from model evaluation and optimization through production infrastructure and ongoing performance management.
We evaluate Llama 3, Mistral 7B/22B, Mixtral 8x7B, Phi-3, and domain-specific models against your accuracy requirements, latency budgets, and hardware constraints.
We implement GGUF (llama.cpp), GPTQ, AWQ, and INT4/INT8 quantization strategies with benchmark validation, delivering the optimal accuracy-vs-hardware tradeoff for your use case.
We deploy and optimize vLLM, Hugging Face Text Generation Inference, and NVIDIA Triton with continuous batching, paged attention, and flash attention configured for your workload.
We build retrieval-augmented generation pipelines with vector database integration (Pinecone, Weaviate, pgvector) that ground LLM responses in your proprietary knowledge base.
EaseCloud's LLM team combines model architecture understanding with systems engineering depth, delivering self-hosted deployments that match or exceed the reliability of commercial API providers.
We maintain hands-on expertise across the Hugging Face ecosystem, llama.cpp, vLLM, and TGI, tracking model releases and inference optimization techniques as they evolve.
We implement flash attention, speculative decoding, continuous batching, and custom CUDA kernels that push inference throughput to the hardware limits of your GPU fleet.
We deploy LLM infrastructure within your security perimeter: air-gapped environments, VPC isolation, mTLS authentication, and audit logging that satisfy SOC 2 and HIPAA requirements.
We design retrieval-augmented generation pipelines with hybrid dense/sparse retrieval, re-ranking, and context management strategies that maximize response quality on domain-specific knowledge.
We precisely model the cost-per-token economics of self-hosted vs API-based LLM delivery, quantifying the break-even point and projected savings at your usage scale.
A rigorous, benchmark-driven process that delivers production-ready LLM infrastructure with predictable timelines and measurable performance guarantees.
We define latency SLAs, throughput requirements, accuracy benchmarks, and hardware budgets, then evaluate candidate models against your specific tasks to identify the optimal base model.
We apply quantization strategies and measure the accuracy-performance tradeoff, selecting the configuration that meets your quality requirements at minimum hardware cost.
We deploy the inference server stack with Kubernetes orchestration, networking configuration, TLS termination, authentication, and monitoring instrumentation within your cloud account or data center.
We optimize KV cache sizing, batch parameters, and concurrency settings against your real workload patterns, validating that throughput and latency targets are consistently met.
We implement inference-specific observability covering tokens-per-second, time-to-first-token, queue depth, and GPU utilization, with alerting and runbooks for rapid incident response.
Find answers to common questions about our cloud consulting services and solutions.
Self-hosting makes economic sense when your monthly API spend exceeds $5,000–$10,000, when your data cannot leave your security perimeter, or when you need customization through fine-tuning. API providers offer superior economics for low-volume, variable workloads and teams without GPU infrastructure expertise. We provide a quantitative break-even analysis before recommending either path.
The right model depends on your task requirements, latency budget, and hardware constraints. Llama 3 70B delivers near-GPT-4-level performance for complex reasoning tasks. Mistral 7B and Phi-3 Mini deliver strong performance for simpler tasks on consumer GPUs. Mixtral 8x7B offers a strong accuracy-vs-speed tradeoff for most enterprise applications. We run task-specific benchmarks before recommending a model.
A 70B model in FP16 requires approximately 140GB VRAM, typically 2x NVIDIA H100 80GB or 4x A100 80GB GPUs. With INT4 quantization (AWQ or GPTQ), VRAM requirements drop to 35–40GB, enabling deployment on a single H100 or 2x A100 40GB. We validate hardware requirements against your throughput targets before finalizing infrastructure specifications.
A standard deployment with an existing open-source model takes 2–4 weeks from kickoff to production, including model evaluation, quantization, infrastructure provisioning, and performance validation. Deployments requiring custom fine-tuning typically add 4–8 weeks for dataset preparation and training. We provide milestone-based timelines after the requirements discovery phase.
Yes. We expose self-hosted LLMs through OpenAI-compatible REST APIs, enabling drop-in replacement of existing OpenAI integrations without application code changes. We also implement streaming responses, function calling, and embedding endpoints that match the OpenAI API specification.
Yes. We offer managed update services covering model version upgrades, security patches, infrastructure scaling, and performance re-optimization as new techniques emerge. Many clients retain us for quarterly model refresh cycles as the open-source ecosystem continues to rapidly advance.