Stop paying per-token to a remote API. Drop GPU usage to 10% of what it was. Pre-pay your inference flat, run it locally, keep every prompt on your machine. From $4.99 / day or $39.99 / month.
Live, on a 4-vCPU AMD virtual machine with no GPU. The endpoint you're hitting is the same server that serves this page. Type a question, click Run.
The four buttons below each load a question. The first three return instantly. The fourth (dashed border) forces a CPU LLM run on the host — Phi-3.5 mini generating tokens at native CPU speed, so you can see real-time generation latency on commodity hardware.
Rate-limited to 5 requests per minute per IP. Common questions return instantly. The dashed-border button forces a fresh CPU LLM run — 10–25 seconds for a full response on this hardware, which is honest CPU speed for Phi-3.5 mini on a 4-vCPU host. The .deb you install runs the same way and scales with whatever cores your machine has.
We don't sell you faster GPUs. We make the GPUs you already own run three times more work. Here's what that looks like in practice.
$50–$200 / month on hosted-API tokens. Prompts logged in the cloud. Rate-limited when you're in flow.
$39.99 / month flat for 150M tokens. 3× faster generation. Local. Same OpenAI-compatible API your existing tools already speak.
GPU pegged at 100% during inference. Ops costs creeping up. Every new feature competes with the last one for the same compute.
GPU at 10% peak. The same hardware handles roughly 10× more work in parallel. Predictable monthly cap — no surprise overage on a successful product launch.
Wait minutes for tokens. Run fewer experiments. Abandon ideas that need throughput you can't justify renting from the cloud.
3× the iterations in the same wall-clock window. Burst freely against the cap. Same prompts, three times the data, your own machine.
Six properties that hold across every supported model. The mechanism is sealed; the results are measured against published benchmarks anyone can re-run.
Three times the tokens per second from the same model on the same machine. The exact gain varies by model and hardware, but customers see consistent 2.5×–4× across the supported list. The bigger your model, the larger the absolute time savings.
The GPU only runs the small residual compute that genuinely needs it. Power draw, fan noise, and thermal headroom all improve. The same card serves 10× more parallel sessions.
Mistral, Qwen, Phi, Llama, DeepSeek, and the rest of the major open-source families. Pull a one-time companion file for the supported models, point Accelerate at it, you're done. Bring your own GGUF if you have one.
OpenAI-compatible HTTP server on localhost:8080. Change one URL in your existing client (Python SDK, fetch call, IDE plugin, anything that speaks OpenAI's protocol) and your stack keeps working. No new client SDK, no migration.
Runs entirely on your hardware. Your prompts never reach our servers — or anyone else's. The package opens no outbound connections at inference time, and we collect no telemetry on what you run or what you ask.
Have a custom or proprietary model we don't pre-build for? The training tool ships in the .deb. Self-built companion files give you the same 3× multiplier on your own model files — useful for fine-tunes, internal models, and one-off research weights.
The same binary works on a system without a GPU. Same encrypted runtime, same secured engine, same Accelerate functionality — running natively on CPU. A five-year-old laptop returns the same answers as an A100 rig. Up to 5× average on commodity CPUs over native CPU inference alone.
Companion files ship for the open-source models below. Pull one with a single command — Accelerate verifies it and serves immediately. Anything we don't pre-build is one self-train run away.
Want a model added to the pre-built list? Email us — we add models based on customer demand and our build queue.
Per-million-token cost across the options. Hosted-API rates are blended public list prices as of 2026-05-02 — yours may differ by exact model and discount tier. Accelerate rates are our published flat-rate pricing amortized over the period's token cap.
| Provider | Per million tokens | What you get |
|---|---|---|
| Anthropic Claude (flagship) | ~$15 in / ~$75 out | Cloud-hosted, prompts may be reviewed for safety |
| OpenAI GPT (flagship) | ~$10–30 blended | Cloud, latency depends on region + tier |
| Open-source via OpenRouter / Together / Fireworks | ~$0.20–3 | Cloud, your prompts touch the provider's infrastructure |
| Validiti Accelerate · Day pass | $1.00 | Local, GPU or CPU, any open-source model, 5M-token cap |
| Validiti Accelerate · Monthly | ~$0.27 | Local, GPU or CPU, any open-source model, 150M-token cap |
| Validiti Accelerate · Yearly | ~$0.16 | Local, GPU or CPU, any open-source model, 1.825B-token cap |
The token cap is gross for the period — burst freely within it. Hosted API comparisons reflect typical workloads where output tokens dominate; for input-heavy use you'd see different blends. Open-source-via-cloud is the closest comparable category to Accelerate (same models, different hosting); self-hosted Accelerate is roughly 10–100× cheaper than cloud-hosted equivalents at meaningful volume.
A note on commodity hardware. Running modern open-source LLMs at usable interactive speed on a CPU-only system has not been a real option since LLMs began. Validiti Accelerate makes it one — same binary, full Accelerate functionality natively on CPU, same prices as the GPU-equipped install. We don't penalize you for not owning a GPU.
Most LLM compute today is spent on tokens the model has seen a thousand times — predictable patterns, common boilerplate, the part of language that's already structurally solved. Accelerate handles those locally because they're cheap to handle locally.
What you send to a cloud LLM, when you send anything at all, is the part that's actually novel — the edge-of-distribution prompts their parameter count was built for. The cloud benefits: less compute spent on rote work, cleaner training feedback from genuinely hard queries, better signal-to-noise in the data their users actually need help with.
Validiti Accelerate isn't a replacement for the central LLMs. It's a quieter base layer that lets the loud ones be useful for the work they're best at. Each layer does what it's best at; the system is healthier as a whole.
Wall-clock period. Token cap is the total for the period — burst freely within.
Whichever expires first ends the pass — buy another to continue.
United States only at launch.
Education or nonprofit?
Contact us
for a generous case-by-case discount.
The enterprise tier addresses larger models, distributed deployments, and capacity beyond consumer caps — sealed by exclusive license auction.
View the auction →Two walls of defense plus one structural promise. What is yours stays yours — the runtime never sends your prompts, your models, or anything derived from them, unless you explicitly route a query to a cloud LLM. The two walls below (one you can see, one you can't) both ship on every tier, from free trial to Yearly.
Pick the track that matches how you work. Both run the same engine, same license, same pricing. The desktop track is the click-and-go evaluation; the server track is the workhorse for production tools that already speak OpenAI.
click-and-go eval
A double-clickable Linux .deb that opens a window: type a prompt, see standard inference vs. Accelerate side-by-side on the same model, on your hardware. Llama-3.2-3B model bundled. Best for first-time evaluation.
openai-compatible /v1/*
A headless Linux .deb managed by systemd. Listens on
localhost:8080
with /v1/chat/completions,
/v1/completions,
/v1/models.
Drop in for any tool that speaks OpenAI — Aider, Cursor,
OpenWebUI, ollama-compat clients, your own scripts.
Best for daily production use.
Both tracks ship the same sealed engine, the same machine-bound activation, and the same Titus runtime watcher. Pick one or run both on different hosts — one license, one machine per activation.
We are not shipping downloads yet. The live demo above runs on the same engine. Reserve your launch slot: contact@validiti.com
