Serve models
at full throttle.
ZSE is a production-first inference engine: memory-efficient serving, cold starts in seconds and GPU-cluster deployment with tensor parallelism built in. More tokens per second, less idle GPU.
Live serving
qwen2.5-7b · A100 · int4
8,420
requests
142ms
p95
3 · TP
GPUs
Live in seconds, not minutes
Time to first token from a cold GPU — Qwen2.5-7B, INT4, on a single A100. ZSE boots while the others are still loading weights.
lower is better · single A100 · INT4
19× faster cold start61
tok/s
vs 57 vLLM
7s
cold start
vs 134s
4×
smaller
INT4 weights
3
GPUs
tensor parallel
Split one model across every GPU
ZSE shards weights and attention across your cluster so a single model runs in parallel on every GPU. Bigger models fit, latency drops, and utilization stays pinned near 100%.
- Weights sharded per device
- Multi-GPU & multi-node clusters
- Near-linear throughput scaling
shard 1/3
shard 2/3
shard 3/3
One command to production
Load model
HF or local checkpoint
Quantize
INT4 · 4× smaller
Shard
tensor parallel
Cold start
ready in 7s
Serve
OpenAI-compatible API
$ zse serve qwen2.5-7b --quantize int4 --tp 3An engine that earns its GPUs
Memory efficient
INT4 quantization and paged attention fit bigger models on the GPUs you already have — no over-provisioning.
Fast cold start
Stream weights and warm kernels in seconds, so autoscaled replicas come online before your queue backs up.
Tensor parallelism
Shard a single model across many GPUs and nodes with near-linear scaling and NVLink all-reduce.
OpenAI-compatible
Drop-in /v1/chat/completions and /v1/embeddings endpoints — point your existing SDKs at ZSE and go.
Multi-model
Host many models behind one engine with hot-swapping and per-model routing, all from a single deployment.
Autoscaling
Scale replicas to traffic and back to zero when idle, so you only pay for the GPU seconds you actually serve.