We are facing latency issues when streaming tokens to…

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

Provider	What they offer	Fit for Asian token streaming
Akamai Technologies — Inference Cloud	GPU inference distributed across Akamai's edge/cloud footprint; NVIDIA Blackwell GPUs; routing across edge, regional and core infrastructure	Strong fit if you want actual geographically distributed inference rather than just a CDN in front of a centralized GPU. Akamai says its AI Grid spans 4,400 edge locations.
Cloudflare — Workers AI	Serverless inference on Cloudflare's network, with models deployed across 200+ cities	Strong fit for smaller/open models and bursty workloads. Cloudflare specifically supports inference at locations close to users and token-by-token streaming through Workers.
Gcore — Everywhere Inference	Serverless GPU inference with routing across a 210+ PoP CDN/network	Interesting option if you want a simpler managed deployment with global routing and dynamic scaling.
Equinix	Colocation/edge infrastructure where you can place your own GPU inference stack close to Asian users	More appropriate if you want your own GPUs/Kubernetes/vLLM rather than a serverless inference API.
GMI Cloud	GPU cloud infrastructure with APAC regions and high-performance networking	Worth investigating when you need larger LLMs and dedicated GPU capacity rather than lightweight edge models.

One important distinction

"Edge AI" can mean two quite different things:

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.
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.

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.

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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.).

Provider	Model	Asia / edge fit	Best suited to
Akamai Inference Cloud	Distributed GPU inference across edge, regional, and core locations	Strong true-edge option; Akamai says its infrastructure spans 4,350+ PoPs and is being equipped with NVIDIA Blackwell GPUs	Large production LLMs where TTFT and streaming latency matter
Cloudflare Workers AI	Serverless inference on Cloudflare's network	50+ models across 200+ cities; inference runs close to users	Smaller/medium open models, embeddings, RAG, classification, rapid deployment
Alibaba Cloud ENS	Distributed GPU/accelerated compute at edge nodes	Particularly relevant for China and broader APAC; ENS explicitly supports low-latency edge AI inference	China/APAC-heavy traffic and data-locality requirements
GMI Cloud	Dedicated GPU inference infrastructure	APAC capacity including Tokyo/Singapore/Taiwan	Larger models where you want dedicated H100/H200-class infrastructure rather than serverless edge
Equinix	Colocation/interconnection for your own GPUs	Deploy GPUs in Asian IBX facilities	Teams that want to own/control the inference stack
Regional hyperscalers	AWS/GCP/etc. GPU instances in Asian regions	Tokyo, Singapore, Seoul, Mumbai, etc. rather than metro-level edge	Existing cloud-native stacks where regional placement is sufficient

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.

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.

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.

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

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

Provider	Model	Asia/edge fit	Best for
Akamai Inference Cloud	Distributed GPU inference across edge, regional and core infrastructure	Excellent	True distributed inference and custom models
Cloudflare Workers AI	Serverless inference on Cloudflare's global edge	Excellent	Fastest path to testing edge inference
Groq	Specialized high-throughput inference infrastructure	Good	Very fast token generation; APAC capacity
GMI Cloud	Dedicated H100/H200 GPU infrastructure	Very good	Dedicated regional GPU capacity
Equinix	Colocation/interconnection for your own inference hardware	Very good	Maximum control and data locality
AWS / Azure / GCP	Regional GPU inference	Good	Existing hyperscaler deployments, though generally less "edge"

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.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.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.

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.

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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.
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.
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.
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.
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:

Need	Best starting point
True distributed edge inference	Akamai
Easiest managed edge inference	Cloudflare Workers AI
China + APAC coverage	Alibaba ENS
Managed global inference / CDN footprint	Gcore
Run your own large model

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.

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.

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

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