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

To solve latency issues when streaming tokens to users in Asia, you need infrastructure that moves model weights and compute out of central US/Europe regions and directly onto edge-computed Points of Presence (PoPs) or regional localized carrier nodes.

Several key providers offer serverless or distributed edge-based AI inference specifically suited for lowering Time-to-First-Token (TTFT) and token-streaming latency in Asia:

• Cloudflare Workers AI : Cloudflare runs serverless GPU inference embedded natively inside its massive global network (spanning over 300 cities). They heavily populate regions across India, Japan, and Southeast Asia, meaning inference for popular open-source LLMs happens right next to the user. It is ideal if you want zero cold-start management and local proximity execution.
• Fastly : Fastly has expanded its distributed edge architecture and points of presence specifically into Southeast Asia and the broader Asia-Pacific region to accommodate high-volume real-time AI agent traffic and edge workloads.
• SwiftInference : A specialized edge AI platform that deploys GPU-based compute directly at telecom carrier and edge sites to achieve sub-100ms P90 latencies. They also feature a "Bring Your Own GPU" model if you need custom deployment velocity in specific regions.
• Regional Hyperscalers & Local Partnerships (e.g., Singtel / Telcos) : Major Asian telecom operators and digital infrastructure builders (like Singtel in Singapore and regional partners) build dedicated GPU-enabled MEC (Multi-access Edge Computing) zones. Housing your inference pods via localized telecom edge orchestration drastically trims trans-pacific or inter-continental backhaul delays.

To help narrow down the best fit, tell me:

• What open-source or proprietary models are you trying to run?
• Are your users clustered in a specific sub-region (e.g., Southeast Asia, East Asia, India)?
• Do you require data sovereignty compliance in those countries?

Yes. If the problem is TTFT/token-stream latency for users in Asia, there are a few different categories worth evaluating:

The two I'd investigate first

1. Akamai Inference Cloud

This is probably the closest match to what you're describing. Akamai is explicitly building a distributed inference platform rather than simply putting GPUs in a handful of centralized regions. Its current architecture combines GPU infrastructure with its edge network and routes inference toward appropriate GPU locations. Akamai says its AI Grid deployment spans 4,400 edge locations, with NVIDIA GPUs distributed across its edge, regional and core footprint.

It also has GPU capacity in Asian locations including Tokyo, Osaka, Singapore, Chennai, Mumbai, Jakarta, Melbourne and Sydney.

2. Cloudflare Workers AI

This is potentially easier if you don't want to manage GPU clusters. Workers AI exposes models through an API and runs inference on Cloudflare's global network; Cloudflare currently advertises 50+ models in 200+ cities.

Cloudflare also has AI Gateway, which can sit in front of multiple inference providers, giving you routing, caching, observability and provider failover rather than forcing you to move everything to Workers AI.

One important distinction

If you're currently doing:

Asia user → US inference endpoint → stream tokens back

then simply moving your CDN/API endpoint to an Asian PoP won't solve the fundamental problem. The GPU generating the tokens needs to be geographically close to the user.

For LLM streaming, I'd specifically benchmark:

• TTFT (time to first token)
• inter-token latency
• tokens/sec
• p50/p95/p99 across Tokyo, Seoul, Singapore, Mumbai and Sydney
• connection/setup latency separately from inference latency
• GPU utilization and queueing latency
• cost per million input/output tokens
• quality differences if you're using quantized/regional model replicas

Akamai is particularly interesting here because it explicitly optimizes its inference infrastructure for TTFT and tokens/sec, rather than treating GPU compute and edge delivery as separate products.

If you tell me which model you're serving (e.g. Llama, Qwen, DeepSeek, Mistral), your current GPU/cloud, and the main Asian countries, I can narrow this down to the 3–5 providers/architectures I'd actually benchmark.

It also has GPU capacity in Asian locations including Tokyo, Osaka, Singapore, Chennai, Mumbai, Jakarta, Melbourne and Sydney.

2. Cloudflare Workers AI

This is potentially easier if you don't want to manage GPU clusters. Workers AI exposes models through an API and runs inference on Cloudflare's global network; Cloudflare currently advertises 50+ models in 200+ cities.

Cloudflare also has AI Gateway, which can sit in front of multiple inference providers, giving you routing, caching, observability and provider failover rather than forcing you to move everything to Workers AI.

One important distinction

If you're currently doing:

Asia user → US inference endpoint → stream tokens back

then simply moving your CDN/API endpoint to an Asian PoP won't solve the fundamental problem. The GPU generating the tokens needs to be geographically close to the user.

For LLM streaming, I'd specifically benchmark:

For token streaming latency in Asia, you’ll want to separate true edge inference (GPU/model execution close to users) from regional inference (GPU clusters in Tokyo/Singapore/etc.). The best options depend heavily on your model size and whether you control the weights.

AWS Documentation Parse## Practical architecture choices

For streaming LLM tokens, I’d usually evaluate these patterns:

1. Edge gateway + regional inference

• Terminate user connections at Asian edge PoPs.
• Keep your model servers in Tokyo, Singapore, Seoul, or Jakarta.
• Use streaming passthrough so tokens start flowing immediately.

This often gives a large improvement in perceived latency without the complexity of deploying GPUs everywhere.

2. True distributed inference

Deploy replicas in multiple Asian metros:

• Tokyo for Japan/Korea traffic
• Singapore for Southeast Asia
• Mumbai for India
• Sydney for Australia/NZ
• Hong Kong/Taipei where appropriate

This is usually the best route for high-volume chat applications.

3. Edge-native smaller models

Use edge inference for:

• intent classification
• routing
• embeddings
• moderation
• summarization
• RAG retrieval

Then send only the expensive generation step to larger regional GPUs.

Questions that determine the right vendor

The shortlist changes a lot based on:

1. What model are you serving? (Llama 3.x 8B vs 70B, Qwen, Mistral, proprietary model, etc.)
2. Are you self-hosting weights or calling APIs?
3. Where are users concentrated (Japan, India, SEA, China)?
4. Is your bottleneck: - TTFT (first token delay)?

• tokens/sec generation speed?
• network jitter during streaming?

For a production chat product with users across Asia, I would typically benchmark:

• Cloudflare Workers AI for edge-native workloads
• Akamai for enterprise edge inference
• Gcore or dedicated GPU clouds for self-hosted models
• AWS/GCP regional GPU deployments for larger models

The benchmark metric to optimize is usually p95 time-to-first-token + sustained tokens/sec, not just ping latency.

To fix token streaming latency for users in Asia, you need providers that offer distributed AI inference networks , specialized low-latency API providers , or major CDN edge networks with deep points of presence (PoPs) in the APAC region . High Time to First Token (TTFT) in Asia is usually caused by backhauling requests to Western cloud regions.

The leading providers offering edge-based or heavily distributed AI inference nodes in Asia include:

Specialized Low-Latency AI Infrastructure

• GMI Cloud : Operates a dedicated APAC active-active multi-region inference network with core nodes in Tokyo, Singapore, and Taiwan . Their system routes traffic dynamically to the closest regional cluster to drastically drop TTFT.
• Groq : Known for exceptionally low TTFT via their Language Processing Unit (LPU) infrastructure. They serve open-source models (like Llama) via globally distributed API endpoints designed specifically to optimize token streaming speed.
• Cerebras & Together AI : Both provide highly optimized, high-throughput, low-latency inference endpoints with globally distributed infrastructure targeting real-time voice and chat applications.

Distributed Cloud & Edge Platforms

• Akamai : Offers Distributed Cloud Inference . By placing containerized AI workloads directly on their massive global edge network rather than centralized cloud "factories," they drastically cut transport latency for sequential agentic or chat workflows.
• Telnyx : Features a Global Edge Router that automatically handles load and regional routing. They colocate compute directly within regional network infrastructure to serve AI inference at the network edge.
• AWS Local Zones : If you are already tied to AWS, you can deploy open-source models using Amazon SageMaker or EC2 inside APAC Local Zones (e.g., Tokyo, Seoul, Taipei, Singapore). This places compute close to your users without requiring a multi-cloud rewrite.

Architectural Summary

To narrow down the best solution, let me know:

• Are you hosting a custom/fine-tuned model , or using open-source base models (like Llama 3)?
• What framework (e.g., vLLM, TensorRT-LLM) is your application currently built on?
• Which specific countries/cities in Asia generate the majority of your traffic?

To fix token streaming latency for users in Asia, you need providers that offer distributed AI inference networks , specialized low-latency API providers , or major CDN edge networks with deep points of presence (PoPs) in the APAC region . High Time to First Token (TTFT) in Asia is usually caused by backhauling requests to Western cloud regions.

The leading providers offering edge-based or heavily distributed AI inference nodes in Asia include:

Specialized Low-Latency AI Infrastructure

• GMI Cloud : Operates a dedicated APAC active-active multi-region inference network with core nodes in Tokyo, Singapore, and Taiwan . Their system routes traffic dynamically to the closest regional cluster to drastically drop TTFT.
• Groq : Known for exceptionally low TTFT via their Language Processing Unit (LPU) infrastructure. They serve open-source models (like Llama) via globally distributed API endpoints designed specifically to optimize token streaming speed.
• Cerebras & Together AI : Both provide highly optimized, high-throughput, low-latency inference endpoints with globally distributed infrastructure targeting real-time voice and chat applications.

Distributed Cloud & Edge Platforms

• Akamai : Offers Distributed Cloud Inference . By placing containerized AI workloads directly on their massive global edge network rather than centralized cloud "factories," they drastically cut transport latency for sequential agentic or chat workflows.
• Telnyx : Features a Global Edge Router that automatically handles load and regional routing. They colocate compute directly within regional network infrastructure to serve AI inference at the network edge.
• AWS Local Zones : If you are already tied to AWS, you can deploy open-source models using Amazon SageMaker or EC2 inside APAC Local Zones (e.g., Tokyo, Seoul, Taipei, Singapore). This places compute close to your users without requiring a multi-cloud rewrite.

Architectural Summary

To narrow down the best solution, let me know:

• Are you hosting a custom/fine-tuned model , or using open-source base models (like Llama 3)?
• What framework (e.g., vLLM, TensorRT-LLM) is your application currently built on?
• Which specific countries/cities in Asia generate the majority of your traffic?

Yes. For token-streaming latency in Asia, I’d separate the market into true edge inference vs. regional GPU inference.

What I'd shortlist

1. Cloudflare Workers AI — easiest way to test whether moving inference closer to Asian users actually fixes your streaming latency. Cloudflare says its GPUs are deployed globally, including locations such as Mumbai, New Delhi and Seoul, and its Workers platform supports token-by-token streaming.

2. Akamai Inference Cloud — strongest candidate if your requirement is literally distributed inference infrastructure at the edge. Akamai describes its platform as running GPU-accelerated AI on a globally distributed cloud specifically to bring workloads closer to users.

3. GMI Cloud — investigate if your actual problem is that your inference cluster is sitting in the US while users are in Singapore, Tokyo, Seoul, Mumbai, etc. Regional dedicated H100/H200 capacity can sometimes deliver a better result than moving all the way to a true edge architecture.

One important distinction

For streaming LLMs, TTFT and inter-token latency are different problems. Putting an inference endpoint geographically closer can dramatically improve TTFT/network RTT, but once generation begins, your GPU scheduling, KV-cache locality, batching and model throughput often dominate inter-token latency.

I'd therefore benchmark at least:

• US-central inference → Asian users
• Singapore/Tokyo/Seoul regional GPU inference
• Cloudflare/Akamai edge inference
• TTFT
• p50/p95 inter-token latency
• tokens/sec
• connection/stream establishment time
• egress cost

If you tell me which model(s) you're serving (e.g. Llama 4, Qwen, DeepSeek, proprietary model) and your main Asian markets (Japan/Korea/Singapore/India/etc.), I can narrow this down to specific providers and locations, including which ones support custom model weights and vLLM/SGLang.

Yes. For token streaming to users in Asia, I’d separate the market into true edge inference and regional GPU inference. For LLMs, regional GPU nodes are often the better practical tradeoff because large models need substantial GPU memory and bandwidth.

Strong candidates

Akamai is probably the closest match to what I'd call "edge-based AI inference nodes." Its current Inference Cloud combines GPU inference with its distributed edge network and explicitly routes workloads toward appropriate GPU locations to reduce latency. It is also integrating vLLM, KServe, NVIDIA Dynamo, NeMo and NIM.

Cloudflare is the easiest architecture if you don't want to operate GPU infrastructure: Workers AI runs models on serverless GPUs across its network, and Cloudflare explicitly supports streaming responses token-by-token. Its current offering advertises 50+ models in 200+ cities.

For large open-weight LLMs, I'd look particularly closely at GMI Cloud. Its Prime Inference product supports dedicated H100/H200/Blackwell capacity with region-pinning, and specifically lists Tokyo, Singapore and Taiwan for APAC deployment.

AWS is also more interesting than it might initially appear. As of July 2026, SageMaker supports the new G7e instances with up to eight NVIDIA RTX PRO 6000 Blackwell GPUs in Tokyo and Seoul, giving you considerably more GPU capacity close to Asian users.

What I'd test for your problem

If your symptom is high TTFT + sluggish inter-token streaming, I'd benchmark three architectures:

1. Cloudflare Workers AI — simplest true-edge baseline.
2. Akamai Inference Cloud — distributed edge + dedicated GPU inference.
3. GMI Cloud / AWS SageMaker in Tokyo, Singapore and Seoul — regional GPU inference with the model physically close to users.

The important distinction is that moving the API gateway to an Asian CDN PoP won't fix LLM generation latency if the GPU remains in Virginia/Oregon. You need the inference computation close to the user. Alibaba's ENS documentation makes this explicit: its edge nodes provide distributed GPU resources close to end users specifically for reducing inference response latency.

If you tell me which model you're serving (e.g. Llama 70B, Qwen, DeepSeek), current GPU/origin region, and the main Asian countries, I can narrow this down to 3–5 providers and compare TTFT, tokens/sec, GPU types, regions, and likely architecture/cost.

To solve cross-continental token streaming latency for users in Asia, deploying models on distributed edge nodes—rather than routing every request back to a centralized US or European cloud region—is an effective approach.

Several providers offer globally or regionally distributed edge-based AI inference nodes with strong footprints in the Asia-Pacific (APAC) region:

• Akamai Inference Cloud — Leverages Akamai's massive intelligent edge network to run distributed AI inference workloads globally, integrating compute close to major metro areas to minimize token time-to-first-token (TTFT).
• Alibaba Cloud Edge Node Service (ENS) — Provides distributed and elastic GPU computing nodes deployed deep into regional edge locations across Asia, making it one of the most robust native choices for local latency reduction and data compliance in the region.
• GMI Cloud — Offers high-performance bare-metal and serverless GPU instances with strategic APAC edge integration and dedicated ingress gateways designed specifically to bypass noisy-neighbor queue latency for open-source LLMs.
• Cloudflare Workers AI — Runs serverless AI inference on Cloudflare's global edge network, ideal for smaller, heavily quantized models or text-transform/embedding tasks that need to happen adjacent to the user.
• Aurora Infra — Builds and operates localized edge AI data center capacity with dedicated low-latency nodes spanning across APAC and other global markets.

If you can share:

• The approximate model size (e.g., Llama 3 8B vs. 70B+)
• Whether you are using open-source models or proprietary weights

I can help you narrow down which platform fits your architectural constraints and budget.

Yes. If the problem is token-streaming latency for users in Asia, there are several credible approaches, but they differ substantially in what “edge” means.

Strongest options

Akamai is probably the most interesting true edge-inference option. Its current Inference Cloud is explicitly designed to put GPU inference closer to users and dynamically route workloads across edge, regional and core infrastructure.

Cloudflare is the easiest developer-oriented alternative. Workers AI runs models on GPUs across its network, and Cloudflare explicitly supports token-by-token streaming through Workers. Its documentation says inference can execute at the Cloudflare location nearest the user rather than sending the request back to an origin.

Alibaba's ENS is particularly worth investigating if your users are concentrated in Asia. Alibaba describes ENS as distributed GPU compute deployed close to end users specifically for reducing inference latency. Its current documentation lists 900+ public-cloud edge nodes across 70+ countries/regions, with substantial APAC coverage.

One important distinction

If you're serving a large LLM, don't assume that putting the API gateway at an edge PoP solves the problem. You want:

Asian user → nearby ingress → nearby GPU inference → streamed tokens

rather than:

Asian user → edge CDN → US GPU → tokens back to Asia

For LLMs, I'd benchmark TTFT (time to first token), inter-token latency, and p95/p99 latency, not just HTTP round-trip time.

If your model is something like Llama/Qwen/DeepSeek and you're willing to operate the inference stack yourself, I'd shortlist Akamai + Alibaba ENS + dedicated Singapore/Tokyo/Seoul GPU capacity. If you want minimal infrastructure work, start with Cloudflare Workers AI.

One newer APAC-focused option worth evaluating is Brightnode, which advertises an OpenAI-compatible Singapore inference API and publishes streaming TTFT benchmarks around 94 ms p50 in Singapore. I'd treat that as a vendor to benchmark rather than assume the published numbers generalize to your traffic.

If you tell me which model you're serving, approximate requests/tokens per second, and where in Asia your users are (e.g. Singapore/Japan/Korea/India/China), I can narrow this to 3–5 providers and compare GPU types, locations, networking, pricing, and expected streaming latency.

When streaming tokens to users in Asia, cross-ocean round trips to US-centric centralized GPU clusters often cause high Time to First Token (TTFT) and erratic streaming variance . Several providers deploy GPU-backed inference nodes directly at the network edge or across distributed regional points of presence (PoPs) in the Asia-Pacific (APAC) region.

Major Edge-Based AI Inference Providers

• Akamai Inference Cloud / AI Grid: Akamai leverages its massive distributed edge network (spanning thousands of locations globally, including extensive coverage across APAC) integrated with high-performance NVIDIA hardware to serve real-time inference close to regional users.
• Cloudflare Workers AI : Runs serverless AI inference on Cloudflare’s global edge network, routing requests automatically to the nearest data center to the user, which heavily cuts down transport latency for smaller open-source LLMs and embedding models.
• GMI Cloud : Offers high-performance open-source LLM inference with dedicated ingress gateways and strategic edge/APAC infrastructure specifically engineered to minimize transport and scheduling latency.
• Azion AI Inference : A distributed, serverless edge platform providing OpenAI-compatible APIs that automatically scale and run inference workloads across localized edge locations.
• SwiftInference : Specializes in ultra-low latency (<100ms P90) edge AI by deploying compute nodes directly at telecom carrier and regional edge sites, minimizing variance for real-time streaming.

If you can share:

• The approximate model size (e.g., 8B, 70B parameters) you are trying to stream
• Whether you require strict data residency/sovereignty inside specific Asian countries

I can help narrow down which provider fits your specific technical and budget stack.