LLM Serving Job Support — Pick and Tune the Right Serving Stack

We provide hands-on, real-time LLM serving proxy job support on your actual project tickets. We help you choose between vLLM, SGLang, and Inference Endpoints, then tune batching, KV cache, parallelism, and quantization for your throughput and latency targets. This page is part of our Hugging Face proxy job support ecosystem: our experts help with architecture and implementation, environment and dependency setup, debugging, code review, performance/GPU-memory tuning, and production issues — during your working hours, same-day. "Proxy" means expert technical support and mentoring on your real deliverables, not replacing you or performing your job duties.

Typical LLM serving production issues we resolve include CUDA out-of-memory and GPU-memory pressure, dependency and version conflicts (Transformers/PyTorch/CUDA), dtype and precision mismatches, slow throughput or latency, checkpoint and safetensors loading errors, and integration failures with upstream and downstream systems. We help you find the root cause from stack traces, logs, and profiling, then ship a stable fix.

Yes. We provide LLM serving proxy interview support (also searched as LLM serving interview proxy support) — real-time expert help on fundamentals, architecture and design trade-offs, scenario-based problems, and hands-on coding rounds — calibrated to the exact role and company format. Proxy interview support means real-time technical help on the exact areas your interview covers; you attend and complete your own interview.

Yes. Onboarding onto an unfamiliar LLM serving setup is one of the most common reasons people reach out. We help you understand the existing model and data pipeline, the training and serving architecture, and the repository structure, get productive fast, deliver your first tasks confidently, and avoid the mistakes that get flagged in reviews and standups.

Contact us on WhatsApp with your stack, the problem, and your timeline. We assign the right expert — usually same-day. Every engagement is fully confidential, and NDAs are available on request.

For new work, use vLLM or SGLang. TGI entered maintenance mode and its GitHub repository was archived, and Hugging Face now recommends vLLM (general serving), SGLang (multi-turn / shared-prefix / agent workloads), and llama.cpp/MLX for local. If you already run TGI, it still works, but we would plan a migration to vLLM or SGLang so you keep getting new-model support and performance improvements. We help with both the migration and the retune.

vLLM is the safe default for general high-throughput generation — mature, broad model coverage, PagedAttention and continuous batching. SGLang shines when you have heavy shared prefixes (long system prompts, agent loops, multi-turn chat) thanks to RadixAttention prefix caching, and for structured/constrained decoding at scale. We benchmark both on your model and traffic shape before you commit.

Self-host (vLLM/SGLang on your own GPUs or Kubernetes) when you need maximum control, custom kernels, or cost efficiency at steady high load. Use Inference Endpoints when you want dedicated, autoscaling, managed infrastructure — including scale-to-zero — without operating servers. We help you model the cost/latency trade-off and pick per workload.