We signed a multi-year agreement to deploy Cerebras wafer-scale inference. Live Q1 2027.Read

We are the neocloudfor alternative chips.

SambaNovaSambaNovaSambaNova

We are the deployment arm for heterogeneous compute.

Backed by

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The hardware

Pick a rack. See what's in it.

This is what you actually buy from us: dedicated racks of purpose-built silicon, one contract, sited with your fleet. Specs are the vendor's own; where a number isn't published, we say so.

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Decode rack · fastest

Cerebras CS-3

One wafer-scale engine per system, all SRAM. The top of the latency ladder for work that can't wait.

Accelerators
2 × CS-3 systems per rack
Power
23 kW per system · ~46 kW per rack
Cooling
Liquid, closed internal loop
Memory
44 GB on-wafer SRAM @ 21 PB/s
Speed
2,522 tok/s per user · Llama 4 Maverick
List price
Not public
Request a quote

Priced per contract, sized to term, volume and site.

Up to 16.1x faster

OpenCode running the identical task on both sides. A real coding session at measured operating points.

OpenCodeGPU cloud today
110 tok/s/user
loop 1 of 32 · 0 tokensstreaming…
OpenCodeGeneral Compute
2,000 tok/s/user
loop 1 of 32 · 0 tokensstreaming…

Prefill vs decode

GPUs are prefill machines. Decode is where they stall.

Prefill is compute-bound and parallel — exactly what a GPU is built for. Decode is memory-bound and autoregressive: one token at a time, limited by bandwidth, not FLOPs. Adding compute does not move it, and the ceiling drops as the model gets larger.

The B300 ceiling sits under

~320tokens / s / user

and materially lower on frontier-scale models. Above it, the GPU cannot operate.

Interactivity frontier — MiniMax M2.7

Output throughput per rack against the per-user token rate it sustains.

SambaNova SN50NVIDIA B300

Everything to the right of the line is a market only purpose-built decode silicon can serve.

GC internal benchmark set, SN50 20kW / 16-RDU decode rack, B300 approximated from SemiAnalysis InferenceX.

Disaggregation

We split the workload. Prefill on GPUs, decode on ASICs.

Prefill

NVIDIA B300 tier

compute-bound, parallel

Decode

SambaNova SN50 · Cerebras · Positron · d‑Matrix

memory-bound, autoregressive

Your surface

Bare metal, or one endpoint if you want it

one contract, one set of SLAs

The offer

We buy the chips. You get the metal.

We own the racks, we do the bring-up, and you get dedicated silicon under contract, sited with your fleet, with root on the machines you're paying for.

You pick a latency target and a price. We put the hardware behind it.

Prefill

NVIDIA B300

Compute-bound. We bring the allocation, so you keep the stack you know.

Fast

SambaNova SN50 · Positron · d‑Matrix

1–2k tok/s/user on multi-trillion-parameter models. SN50 in production first; Positron and d‑Matrix widen the ladder as they reach volume.

Fastest

Cerebras

SRAM wafer-scale, at the top of the ladder for latency-critical work.

What it takes

Hardware, allocation, data centers, software. We run all four.

Siting, bring-up and operations are on us. You are buying capacity under contract, not standing up a build.

In our colocation pipeline

30MW+

01

Hardware

We buy the racks and carry them on our balance sheet. You get dedicated silicon under contract, with root on the machines.

02

Allocation

Price-protected quota across three vendors, placed ahead of merchant availability.

03

Data centers

We handle colocation site selection, power and cooling in existing US facilities, matched to each vendor's requirements. Capacity sits alongside your fleet where possible.

04

Software

Model bring-up on closed vendor stacks, and orchestration of prefill and decode as one service, for the life of the contract.

The business

Every chip that is not a GPU needs a route to market. We're building it.

Tell us the model, the traffic shape and the interactivity target. We'll come back with a rate card and a deployment slot — not a waitlist.

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