vLLM
Engineering.
We deploy and tune vLLM inference clusters to serve open-weight models at a fraction of the cost and latency of hosted frontier APIs, without sacrificing throughput under real concurrency.
PagedAttention at Production Scale
vLLM's core advantage, PagedAttention managing the KV cache like virtual memory pages instead of contiguous blocks, only pays off if the surrounding deployment is actually tuned to exploit it. We size GPU fleets against measured throughput (tokens/sec/GPU under your real prompt-length distribution, not vendor benchmarks) rather than sticker specs, and treat quantization as a per-workload tradeoff between cost and eval-measured accuracy rather than a blanket default.
Continuous batching tuned against your real request pattern: bursty chat traffic and steady batch-extraction workloads need different max-batch-size and scheduling settings.
AWQ or GPTQ 4-bit quantization benchmarked against your own eval set before rollout, since quantization error is task-dependent. We don't ship a quantized model without measuring the accuracy delta first.
Tensor parallelism sized to the model, not the GPU count available. Over-sharding a 7B model across 4 GPUs often loses more to communication overhead than it gains in memory headroom.
Prefix caching for shared system prompts across requests, which alone can cut time-to-first-token by 30-50% on high-volume, templated workloads.
What we build
High-Throughput Serving
Configuring PagedAttention, continuous batching, and tensor/pipeline parallelism to maximize GPU utilization under concurrent load, benchmarked against your actual traffic shape, not synthetic load tests.
Cost-Optimized GPU Sizing
Right-sizing GPU fleets, choosing between A10G/L40S/A100/H100 tiers, and applying AWQ/GPTQ quantization to hit latency SLAs at the lowest verified cost per million tokens.
Zero-Downtime Model Rollouts
Blue-green deployment of new model weights behind a serving layer, gated on automated eval-suite regression checks before traffic shifts, with instant rollback on a metrics breach.
Autoscaling for Bursty Inference
GPU-aware autoscaling (queue depth and TTFT-triggered, not CPU-triggered) so a traffic spike doesn't silently degrade latency for every in-flight request.
What we fix
Hosted API costs that scale linearly with usage with no negotiated ceiling
Sensitive data leaving the VPC to reach a third-party model endpoint
Naive HuggingFace `transformers` serving that falls over or queues badly under concurrent requests
Unpredictable latency spikes with no batching, prefix caching, or queuing strategy
GPU fleets sized off vendor marketing numbers instead of measured throughput on real prompts
No safe rollback path when a newly deployed model checkpoint regresses in production
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.