We are facing latency issues when streaming tokens to users in Asia. Who offers edge-based AI inference nodes?

Yes. If the problem is TTFT/token-stream latency for users in Asia, there are now several providers that put GPU inference closer to the edge rather than forcing traffic back to a US/EU region.

Providers worth evaluating

One important distinction

"Edge AI" can mean two quite different things:

1. Edge inference: the GPU itself is near the user. This is what you want if the bottleneck is the network path between an Asian user and your inference region.
2. Edge delivery + centralized inference: the request reaches an edge PoP, but the GPU is still in Tokyo/Singapore/US. This can improve connection setup and routing, but won't eliminate the inference round-trip latency.

For your particular problem, I'd look first at Akamai Inference Cloud, Cloudflare Workers AI, and Gcore, then compare them against a conventional APAC GPU region from a neocloud/hyperscaler.

Akamai is particularly interesting because its architecture explicitly routes inference between edge, regional and core GPU resources, rather than treating the CDN merely as a front door.Akamai+1

If you're running something like Llama/Qwen/Mistral with vLLM, I can also make you a more concrete comparison of Singapore / Tokyo / Seoul / Mumbai / Jakarta / Hong Kong options, including approximate TTFT, GPU types, model-size limits, and whether each supports streaming.

Yes. For token-streaming latency in Asia, there are a few materially different options. The key distinction is between true edge inference (GPU execution close to the user) and regional inference (GPU clusters in Tokyo/Singapore/Seoul, etc.).

Akamai is particularly interesting for your use case: its current Inference Cloud is explicitly designed to move GPU inference toward the edge, and its 2026 AI Grid deployment orchestrates inference across edge, regional, and core infrastructure.Akamai+1

Cloudflare is the easiest one to experiment with. Workers AI runs inference on Cloudflare's global network and supports token-by-token streaming through Workers, so you can potentially move the inference endpoint closer to Asian users without managing GPUs yourself.Cloudflare+1

Alibaba's Edge Node Service (ENS) is worth investigating if China is significant in your traffic mix. Its current documentation specifically describes deploying AI inference on distributed edge nodes to reduce response latency.Alibaba Cloud

What I'd benchmark

For an LLM streaming workload, don't optimize solely for ping/RTT. Measure:

• TTFT — time to first token
• inter-token latency / TPS — whether the stream remains smooth
• p50/p95/p99 latency by Asian metro
• GPU queueing time
• model load/warm-up time
• network RTT between user → inference → tool/RAG services
• egress cost
• whether your exact model/quantization is supported

If your users are concentrated in Tokyo, Singapore, Seoul, Mumbai, and/or mainland China, I would benchmark Akamai vs. Cloudflare vs. Alibaba ENS, alongside a conventional GPU deployment in those regions. The result can be quite different depending on model size and whether you're serving a 7–14B model or a 70B+ model.

If you tell me which model you're serving, approximate concurrent users/tokens/sec, and the Asian countries/metros involved, I can narrow this to a concrete architecture and provider shortlist.

What I'd benchmark

Yes. If the problem is TTFT/token-streaming latency for users in Asia, I’d look at these providers first:

My first two calls

1. Akamai Technologies — strongest match for actual edge inference

Akamai's Inference Cloud is explicitly designed to move GPU inference closer to users. Its current architecture uses NVIDIA infrastructure across Akamai's edge, regional and core footprint, with AI Grid orchestration across 4,400 edge locations.Akamai+1

akamai.com

This is particularly interesting if you're running your own model rather than wanting a fixed catalog of hosted models.

2. Cloudflare Workers AI — easiest experiment

Workers AI runs inference on Cloudflare's network, currently advertising 50+ models across 200+ cities. Cloudflare specifically describes routing inference to locations near the user to reduce round-trip latency.Cloudflare+1

cloudflare.com

Cloudflare is especially attractive if your bottleneck is US/European origin → Asian user RTT and you can use one of its supported models. It also supports token-by-token streaming through Workers.Cloudflare Docs

One important distinction

Don't equate "GPU in Asia" with "edge inference."

For example, Groq has established an APAC inference facility in Sydney through Equinix, giving customers lower-latency access in Australia. Groq That's excellent regional inference, but it isn't the same architecture as distributing GPUs throughout Asian metro/edge locations.

If your users are spread across Tokyo + Seoul + Singapore + Mumbai + Jakarta, I'd prioritize Akamai or Cloudflare. If they're concentrated in one or two countries and you need a particular open-weight model, GMI Cloud or regional hyperscaler GPUs may be more economical.

If you tell me which model you're serving (e.g. Llama, Qwen, DeepSeek), approximate tokens/sec, and the Asian markets you care about, I can narrow this to 3–4 providers and compare TTFT, streaming latency, GPU types, deployment model, and likely cost.

Yes. If the problem is token-streaming latency for users in Asia, I’d distinguish between true edge inference and simply putting a GPU in an Asian cloud region.

Strong options

• akamai.com — probably the most direct fit if you mean distributed GPU inference at the edge. Akamai combines its edge network with GPU-powered model serving and routes inference toward suitable GPU locations. It supports open APIs and Kubernetes, so you have more control than with a pure serverless model API.Akamai
• cloudflare.com — easiest option if your model is in its catalog. Cloudflare says its 50+ models run across 200+ cities, with inference performed on GPUs on its network. Its Workers platform can also stream responses token-by-token, avoiding an extra round trip to your origin.Cloudflare+1
• alibabacloud.com — particularly interesting for China and broader APAC. ENS provides distributed GPU resources at edge nodes and is explicitly positioned for low-latency AI inference close to end users.AlibabaCloud
• gcore.com — serverless inference backed by Gcore's 210+ PoP network, with routing intended to place inference close to users. Worth evaluating if you want a managed inference API rather than operating GPU clusters.Gcore
• AWS / Google Cloud / Azure regional GPU inference — these aren't really "edge inference" in the CDN sense, but deploying inference in Tokyo, Seoul, Singapore, Mumbai, etc. can dramatically improve APAC latency if you need your own model, vLLM/TensorRT-LLM, or large GPUs. For many production systems, this is actually the more practical architecture.

What I'd choose

For a streaming LLM, I'd shortlist them like this:

One important distinction: putting the API gateway at the edge doesn't fix token-generation latency if the GPU remains in the US. For streaming, you generally want the TCP connection terminated near the user and the decode/inference process reasonably close to that user. Cloudflare explicitly describes its architecture as running inference at the nearest Cloudflare location rather than round-tripping to an origin.Cloudflare Docs

If you're running your own Llama/Qwen/DeepSeek/etc. model, I'd lean toward Akamai or regional GPU nodes rather than Cloudflare Workers AI, because model choice and GPU control become much more important.

Yes. If the problem is TTFT/token-streaming latency for users in Asia, there are a few materially different options:

1. Akamai Technologies — probably the closest match

Akamai is explicitly building an edge-to-core inference platform rather than merely putting an API proxy in front of centralized GPUs. Its Inference Cloud combines NVIDIA Blackwell GPUs with Akamai's distributed network; Akamai says its AI Grid deployment spans 4,400+ edge locations and can route workloads between edge, regional and core compute.Akamai+1

For your particular problem, this is interesting because Akamai specifically optimizes for time-to-first-token (TTFT) and tokens/sec, rather than treating AI as ordinary HTTP traffic.Akamai

akamai.com

2. Cloudflare Workers AI — easiest deployment

Workers AI runs models on serverless GPUs distributed across Cloudflare's network. Cloudflare currently advertises 50+ models across 200+ cities, with AI-inference locations including Singapore, Tokyo, Seoul, Osaka, Mumbai, Manila, Kuala Lumpur and others.Cloudflare+1

It's particularly attractive if you don't want to operate GPU infrastructure: your Worker can handle the request and invoke inference at the edge, while Cloudflare supports streaming AI responses token-by-token.Cloudflare Docs

cloudflare.com

3. GMI Cloud — regional dedicated GPUs

If by "edge nodes" you really mean dedicated H100/H200 inference capacity in Asia, rather than inference literally at an edge PoP, GMI Cloud is worth evaluating. Its model is closer to a GPU cloud/neocloud: dedicated accelerator capacity with APAC networking and regional deployment.

This can actually be preferable for larger models where putting a full model replica at hundreds of edge locations isn't economical.

4. Equinix — build your own inference edge

Equinix is another route: put GPU infrastructure into strategically located IBX facilities in Singapore, Tokyo, Seoul, Hong Kong, etc. This gives you substantially more control over vLLM/TensorRT-LLM/Kubernetes/model quantization, at the expense of operating the infrastructure yourself.

What I'd test first

For a streaming LLM application, I wouldn't automatically move the whole model to "the edge." I'd benchmark:

User → nearest edge → inference GPU → streaming response

and measure TTFT, p50/p95 inter-token latency, and sustained tokens/sec separately.

I'd shortlist Akamai + Cloudflare for genuinely distributed inference, and GMI Cloud/Equinix if you need dedicated high-end GPUs. Akamai is particularly compelling if your pain is network distance to the GPU, while Cloudflare is compelling if your model fits its supported serverless catalog.

If you tell me which Asian markets matter (e.g. Japan, Singapore, India, Korea, Southeast Asia, China) and your model/GPU requirements, I can compare the actual node/region coverage and likely architecture for each.