Put open models to work on your hardware.
Give production agents dedicated inference endpoints, with dynamic kernels and workload-specific optimization for the models and silicon you choose.
An agent is only as responsive as its model calls.
A workflow can make many chained calls. Dedicated capacity and hardware-aware serving give your team a way to tune for latency, throughput and cost on the workloads you actually run.
Kernels compiled for your silicon, not the vendor's demo box.
Most inference stacks pick a lowest-common-denominator kernel and hope it runs everywhere. We do the opposite: the runtime introspects the target hardware, generates kernels tuned for that exact chip, and re-tunes as the workload shifts. Same model, radically different throughput.
New models on day one
MoE, hybrid-attention, Mamba/SSM, diffusion LMs, native multimodal — supported the day the paper drops, no waiting for a vendor SDK to catch up.
Compiled per model, per chip
Fused attention, speculative decoding, quantization-aware kernels and paged KV cache are generated for the exact model + hardware pair — not shipped as a static binary.
Autotuned to your workload
Batch shape, sequence length and concurrency profiles are profiled continuously; the scheduler re-picks kernels and quantization as traffic patterns change.
NVIDIA · AMD · Intel · TPU · Trainium · Groq · Cerebras · CPU
One runtime, one API, one control plane. Bring your existing GPUs, your cloud's accelerators or an air-gapped appliance — the platform adapts.
Public cloud, VPC, on-prem, edge
Deploy the same stack in your VPC, in a sovereign region, in an air-gapped SCIF or at a factory-floor edge box. Nothing leaves your perimeter unless you say so.
Optimized for chained calls
Prefix caching, speculative decoding and cross-request batching are tuned for how agents actually call models — many small chained calls, not one big prompt.
Four ways to run inference for production agents.
Choose the serving pattern that fits the workload, from interactive calls to dedicated capacity.
Real-time inference
Interactive model calls for agent workflows.
Batch inference
Async processing for evaluations and larger workloads.
Post-training & fine-tuning
Fine-tune, distill and align open models on your data. LoRA, SFT, DPO and RL.
Dedicated agent endpoints
Capacity scoped to the workload and deployment environment.
Dedicated agent endpoints — in minutes, not weeks.
Pick a model, region and latency target from the console — the endpoint goes live and agents start routing traffic immediately. Private networking, custom SLAs and full audit from minute one.
A catalog that ships with the frontier.
Discuss model availability and compatibility for your deployment.
OpenAI-compatible. Drop-in in 3 lines.
from openai import OpenAI
client = OpenAI(
base_url="https://api.synaptix.ai/v1",
api_key="sx_live_…",
)
resp = client.chat.completions.create(
model="gpt-oss-120b",
messages=[{"role": "user", "content": "Summarize this report."}],
)
print(resp.choices[0].message.content)curl https://api.synaptix.ai/v1/chat/completions \
-H "Authorization: Bearer $SX_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v3.2",
"messages": [{"role":"user","content":"Hello"}]
}'Bundled with Synaptix Agent Platform.
The Inference Platform isn't sold by the token. It ships as the inference backbone of the Agent Platform — licensed together, deployed together, governed by the same control plane.
Evaluate the Inference Platform
Benchmarks, technical posts and a printable product brief.
Measure inference on your workload
The metrics and evaluation approach to discuss with our team.
Inference Platform and the open-model frontier
gpt-oss, Kimi-K2.5, Qwen3-Coder and GLM-5 in production.
Benchmarking agent latency: TTFT, p95 and what single-model benchmarks miss
Why agent workloads need a different benchmark methodology.
Inference Platform — product brief
Architecture, model catalog, pricing and SLA in one PDF.
Heterogeneous inference — product brief
Routing across NVIDIA, AMD, Cerebras, Groq and TPU.
Plan inference around your agents.
Discuss your models, hardware and capacity requirements with our team.