The world's fastest inference platform — on any hardware.
Latest open-model architectures, dynamic kernel generation and target-specific optimization — so agents run at frontier speed on the silicon you already own, in any cloud, on-prem or at the edge.
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.
From a shared API call to reserved multi-region endpoints — scales with your agent estate.
Real-time inference
Sub-second latency for interactive agents.
Batch inference
Async processing at up to 50% lower cost.
Post-training & fine-tuning
Fine-tune, distill and align open models on your data. LoRA, SFT, DPO and RL.
Dedicated agent endpoints
Reserved capacity in minutes.
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.
New open releases added within days — same API, no migration.
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.
Go deeper on the Inference Platform
Benchmarks, technical posts and a printable product brief.
TTFT, throughput and p95 vs. Bedrock, Vertex and Together
How the Inference Platform compares across the open-model frontier.
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.
Ship with dedicated agent endpoints today.
Spin up an API key in minutes — or talk to us about reserved capacity and fine-tuning.