See how Telnyx’s GPU network can help you overcome the GPU supply crunch to get your AI products to market with ease, at scale.
In today's world, businesses need partners they can trust to stay ahead. At Telnyx, we're not just network experts; we're pioneers in designing the networks of tomorrow. For over a decade, we've established a formidable reputation by building an RTC network and enhancing it with the recent addition of edge-PoPs. Our commitment has always been to provide the infrastructure businesses require, even in the face of adversities like the current GPU supply crunch.
AI has opened up a world of potential to revolutionize industries. However, the increasing demand for NVIDIA's GPUs has posed a challenge, both in terms of accessibility and the cost involved. Many startups today are feeling the pinch of longer times to market and inflated expenses associated with model training and fine-tuning, all due to the scarcity of these vital components.
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Our proactive approach meant we purchased GPUs and built out a network to ensure we never have to rely on a third-party GPU provider. We built Telnyx Inference on top of our GPUs. Our network auto-scales with increased demand, making Telnyx a partner you can rely on.
Here's why partnering with Telnyx for Inference makes perfect business sense:
With NVIDIA GPUs becoming scarce, businesses are feeling the impact on their AI development and go-to-market timelines. By owning our GPUs, we ensure our partners aren't restricted in their AI endeavors—easing the frustration of GPU supply constraints.
The standard financial challenges startups face are exacerbated when developing AI products. By opting to work with Telnyx, companies can realize significant savings. With our owned infrastructure, we've eliminated the middlemen, ensuring that our users only pay for the true value they receive.
For any company in 2024, efficiency is key. Telnyx is committed to offering businesses the shortest time-to-market with our infrastructure accessible through intuitive APIs that are geared for speed, making AI applications more efficient than ever.
Our AI playground makes it easy to test the speed and agility of our tools. Take a look at this 2-minute demo to learn more.
We're actively looking for feedback on Telnyx Inference and want to hear how we can help take your applications to the next level. To learn how you can get the most value out of our GPU network, get in touch with a member of our team today.
Our vision doesn't stop at simply owning GPUs. At Telnyx, we're on a mission to construct our very own GPU network designed specifically to support AI at the edge. Here's the rationale behind our ambitious endeavor:
With a system tailored for vectorization and rapid inference, businesses can harness AI's true potential with ease—through the Mission Control Portal and APIs.
In the world of generative AI applications, latency can be a deal-breaker. By co-locating our GPU and Cloud Storage infrastructure, we'll aim to drastically reduce response times, offering an elevated user experience.
A carefully designed GPU network combined with multi-region Cloud Storage infrastructure, Telnyx offers fast, contextualized Inference at scale to users looking to harness the power of AI, today.
Why choose Telnyx as your underlying provider to build your Generative AI applications? Our network of GPUs enables cost-effective inference, unparalleled autoscaling, and offers a co-location advantage.
With Telnyx, companies of all sizes stand to benefit from considerable savings over using different GPU and Inference providers. Owning our infrastructure, we cut out intermediaries, offering unbeatable value.
Our GPUs aren't just powerful; they're smart. Built to handle high-volume requests, they scale automatically based on workload. The result? Optimal performance, every single time.
With Telnyx's GPUs and Cloud Storage infrastructure situated in close proximity, businesses can transition from data to inference in a flash. Experience real-time inference like never before.
In an AI-driven age, having the right partner can make all the difference. Telnyx, with its forward-thinking approach and robust infrastructure, stands ready to propel businesses who are looking to invest money and resources into AI applications.
What is GPU hosting for inference? GPU hosting for inference is the use of GPU-equipped servers to run trained models in production with low latency. It leverages parallel compute and high memory bandwidth to accelerate LLM decoding, speech, and vision workloads.
Do I need a GPU for inference or can a CPU handle it? CPUs can handle small models and batch jobs, but real-time LLMs, large embeddings, or multimodal tasks usually need GPU throughput to hit sub-second SLAs. Choose CPU-only for low QPS offline workloads, and GPUs when concurrency or latency are critical.
How do I choose the right GPU for inference? Match VRAM to model size and precision, and favor GPUs with strong FP16 and INT8 support, fast HBM, and high memory bandwidth. H100 fits large LLMs and heavy batching, A100 covers mid to large models, and L4 or L40S serve cost-efficient streaming tasks.
How much VRAM do I need for LLM inference? A rough guide is parameters times bytes per weight plus KV cache and headroom, so a 7B model at 8-bit needs about 8 to 10 GB while a 70B at 8-bit can exceed 80 GB. Longer contexts, higher batch sizes, and higher precision increase memory significantly.
What factors most affect inference latency? Latency is driven by model size, precision, batch scheduling, network path, and user proximity to the GPUs. If requests include images or audio delivered as MMS messaging, preprocessing and media transfer time become part of the end-to-end budget.
How should I estimate capacity and cost for GPU inference? Think in tokens per second per GPU, average concurrency, and utilization, then map that to instance pricing and autoscaling strategy. When campaigns fan out requests like broadcast MMS, peak concurrency spikes will drive GPU pool size more than steady-state averages.
How do I integrate GPU inference with messaging or customer engagement workflows? Use event-driven pipelines that accept text and media, normalize payloads, and enqueue inference jobs close to your GPU region, then respond via the same channel. For apps that ingest attachments, align input formats with channel capabilities such as MMS message types so your preprocessing and safety checks are predictable.
