Which AI Inference Provider Delivers the Lowest Latency for Real-Time Applications?

There's no single provider that delivers the lowest latency across every model, every context length, and every concurrency level. Latency depends on hardware architecture, serving optimization, model size, and traffic patterns.

But the question matters because for AI agents and real-time applications, inference speed directly determines user experience, decision quality, and cost.

GMI Cloud, as a GPU infrastructure provider operating owned H100/H200 clusters, plays a foundational role in this equation: the GPU hardware layer determines the latency ceiling that any inference provider can achieve, regardless of their software optimizations.

This article defines what AI agent inference providers do, explains why low latency matters for agents, categorizes the market, introduces GMI Cloud's GPU capabilities for latency-sensitive workloads, establishes evaluation criteria, analyzes seven specialized providers (Cerebras, Groq, Fireworks AI, Together AI, Anyscale, SambaNova, Lepton AI), and provides a multi-provider routing strategy with GMI Cloud as the infrastructure foundation.

What Are AI Agent Inference Providers?

AI agent inference providers are platforms that serve LLM inference via API, optimized for the speed and reliability that autonomous AI agents need.

Unlike self-hosted inference (where you manage GPUs, serving engines, and scaling yourself), these providers handle the full stack: hardware, model loading, request routing, batching, and auto-scaling.

You send a prompt, you get a response, and the latency between those two events determines whether your agent feels instant or sluggish.

The underlying GPU infrastructure is the decisive factor in what these providers can achieve. A provider running on H100 SXM GPUs (3.35 TB/s memory bandwidth) has a fundamentally higher throughput ceiling than one running on A100s (2.0 TB/s).

Software optimizations like continuous batching and PagedAttention improve efficiency within that ceiling, but they can't exceed it. That's where GPU resource providers like GMI Cloud set the foundation.

Why AI Agents Need Fast Inference

Conversational Responsiveness

Users expect sub-second time-to-first-token (TTFT) in chat interfaces. Agents that chain multiple LLM calls (reasoning, tool use, response generation) multiply latency at each step. A 500ms TTFT per call becomes 2 seconds across a 4-step agent loop.

Cutting per-call latency from 500ms to 100ms makes the same loop feel instant.

Decision Quality Under Time Pressure

Agents making real-time decisions (fraud detection, trading signals, content moderation) need results before the moment passes. A fraud detection agent that takes 3 seconds to evaluate a transaction is useless for real-time blocking.

Low-latency inference turns AI from an analytics tool into an operational decision-maker.

Cost at Scale

Faster inference means shorter GPU occupancy per request. If your provider serves tokens 3x faster, each request uses 1/3 the GPU-seconds, which translates directly to lower cost per inference at scale. Hardware optimization (FP8 on H100/H200, high-bandwidth memory) is what makes this speed possible.

Scalability

Low-latency providers that maintain consistent TTFT under high concurrency enable you to scale agent deployments without degrading user experience. This requires both software optimization (efficient batching) and hardware capacity (enough GPU resources to absorb traffic spikes without queuing).

Market Categories: General vs. Specialized Providers

General-purpose providers (OpenAI, Anthropic, Google) prioritize model quality and breadth. They serve the widest range of use cases but don't optimize specifically for latency. TTFT can vary from 200ms to 2+ seconds depending on load.

Speed-specialized providers (Cerebras, Groq, Fireworks) optimize their entire stack for inference speed. They use custom silicon, optimized serving engines, or aggressive hardware configurations to minimize TTFT and maximize tokens per second.

The trade-off: narrower model selection and sometimes limited context windows.

GMI Cloud supports both categories as an infrastructure layer. General-purpose providers can deploy on GMI Cloud's H100/H200 clusters for GPU capacity. Speed-specialized providers can leverage the same infrastructure when their custom hardware reaches capacity limits.

For teams building their own inference stack, GMI Cloud's pre-configured serving tools (vLLM, TensorRT-LLM, Triton on H100/H200) provide a self-managed path to low latency.

GMI Cloud: GPU Foundation for Low-Latency Inference

Core Capabilities

GMI Cloud (gmicloud.ai) provides the GPU hardware layer that low-latency inference depends on.

Its owned H100 SXM (~$2.10/GPU-hour, 80 GB HBM3, 3.35 TB/s) and H200 SXM (~$2.50/GPU-hour, 141 GB HBM3e, 4.8 TB/s) clusters are pre-configured with CUDA 12.x, vLLM, TensorRT-LLM, and Triton, tuned for NVLink 4.0 topology (900 GB/s bidirectional per GPU) and 3.2 Tbps InfiniBand.

Check gmicloud.ai/pricing for current rates.

Latency-Optimized Features

Precision resource allocation: match GPU type to model size so you're not over-provisioning (wasting budget) or under-provisioning (causing memory swaps that spike latency).

Dynamic load balancing: distribute requests across GPU instances based on current queue depth and VRAM utilization, preventing any single instance from becoming a bottleneck. Low-overhead scheduling: reserved instances eliminate cold-start latency for always-on workloads; on-demand instances handle burst traffic.

Target Scenarios

Real-time conversational AI agents, high-concurrency decision inference (fraud, moderation), multi-step agent pipelines where per-call latency compounds, and multimodal applications requiring parallel LLM + vision + TTS inference.

GMI Cloud's Model Library also offers direct API access: GLM-5 (by Zhipu AI) at $1.00/M input and $3.20/M output, 68% cheaper than GPT-5 ($10.00/M), for teams that want managed low-latency endpoints without self-hosting. Check console.gmicloud.ai for availability.

Evaluation Dimensions for Low-Latency Providers

Dimension (What to Measure / Why Hardware Matters)

• TTFT — What to Measure: Time to first output token — Why Hardware Matters: Bounded by GPU memory bandwidth for weight loading
• Throughput (tok/s) — What to Measure: Output tokens per second per request — Why Hardware Matters: Higher bandwidth = faster token generation
• Cost efficiency — What to Measure: $/M tokens at your volume — Why Hardware Matters: GPU utilization rate determines cost floor
• Model selection — What to Measure: Which models at what context lengths — Why Hardware Matters: VRAM limits model size; bandwidth limits context
• Developer experience — What to Measure: API compatibility, docs, SDK quality — Why Hardware Matters: Pre-configured stacks reduce integration time

Seven Low-Latency Providers Analyzed

Cerebras

Cerebras uses its custom Wafer-Scale Engine (WSE) silicon to achieve extremely high throughput on supported models. It delivers some of the fastest tokens-per-second rates available, particularly for Llama-class models.

The limitation: a narrow model catalog and context-length constraints imposed by the WSE architecture. Best for teams that need maximum speed on supported open-source models.

Groq

Groq's custom Language Processing Units (LPUs) are purpose-built for deterministic, low-latency inference. TTFT is consistently fast because the architecture eliminates the memory-bandwidth bottleneck that GPU-based systems face. The trade-off: limited model support and capacity constraints during peak demand.

Best for latency-critical agent loops on supported models.

Fireworks AI

Fireworks optimizes inference on standard NVIDIA GPUs (H100, A100) with custom serving engine optimizations. It offers broader model support than custom-silicon providers while still achieving competitive latency. Supports function calling and structured output natively.

Best for production agent workloads that need speed, model flexibility, and standard GPU infrastructure.

Together AI

Together AI provides optimized inference for open-source models with a strong developer experience. It balances speed and model breadth well, with competitive pricing on popular models (Llama, Mixtral, Qwen). Best for teams running open-source LLMs who want managed inference without extreme latency requirements.

Anyscale

Built on the Ray framework, Anyscale offers scalable inference with fine-grained resource management. Its strength is handling complex multi-model pipelines and custom model deployments. Best for teams with distributed computing expertise who need flexible inference orchestration.

SambaNova

SambaNova's custom Reconfigurable Dataflow Units (RDUs) are designed for enterprise AI workloads with guaranteed SLAs. It targets enterprise customers who need predictable performance and dedicated infrastructure. Best for large enterprises with strict latency SLA requirements.

Lepton AI

Lepton AI focuses on developer-friendly, fast-deploy inference with competitive pricing. It offers a clean API experience and quick onboarding. Best for smaller teams and startups that need fast inference without heavy infrastructure commitment.

Performance Comparison

Cerebras

• Hardware: WSE (custom)
• Speed Focus: Highest tok/s
• Model Breadth: Narrow
• Best Use Case: Max-speed open-source inference

Groq

• Hardware: LPU (custom)
• Speed Focus: Lowest TTFT
• Model Breadth: Narrow
• Best Use Case: Latency-critical agent loops

Fireworks AI

• Hardware: H100/A100
• Speed Focus: High
• Model Breadth: Broad
• Best Use Case: Production agents needing flexibility

Together AI

• Hardware: NVIDIA GPU
• Speed Focus: Medium-High
• Model Breadth: Broad
• Best Use Case: Open-source model serving

Anyscale

• Hardware: NVIDIA GPU
• Speed Focus: Medium
• Model Breadth: Custom
• Best Use Case: Multi-model pipelines on Ray

SambaNova

• Hardware: RDU (custom)
• Speed Focus: High (SLA-backed)
• Model Breadth: Enterprise
• Best Use Case: Enterprise with latency SLAs

Lepton AI

• Hardware: NVIDIA GPU
• Speed Focus: Medium-High
• Model Breadth: Moderate
• Best Use Case: Startup-friendly fast inference

For GPU-based providers (Fireworks, Together, Anyscale, Lepton), performance is directly tied to the underlying GPU infrastructure.

Running these workloads on GMI Cloud's H100/H200 clusters with pre-optimized vLLM and TensorRT-LLM can push latency closer to the hardware ceiling: H200's 4.8 TB/s bandwidth delivers measurably faster token generation than H100's 3.35 TB/s for bandwidth-bound workloads.

For teams self-hosting inference, GMI Cloud provides the GPU layer that these providers build on.

Multi-Provider Routing Strategy

Task Type (Primary Provider / GMI Cloud Role)

• Ultra-low TTFT agent loops — Primary Provider: Groq or Cerebras — GMI Cloud Role: Overflow capacity on H100/H200 when custom silicon is at capacity
• Production chat with model flexibility — Primary Provider: Fireworks AI or GMI Cloud API — GMI Cloud Role: Direct: GLM-5 at $3.20/M output via Deploy endpoints
• High-volume budget inference — Primary Provider: GMI Cloud API — GMI Cloud Role: GLM-4.7-Flash at $0.40/M output (33% cheaper than GPT-4o-mini)
• Multi-model agent pipeline — Primary Provider: Anyscale (Ray) on GMI Cloud GPUs — GMI Cloud Role: H100/H200 infrastructure with NVLink + InfiniBand
• Enterprise SLA-backed inference — Primary Provider: SambaNova or GMI Cloud Deploy — GMI Cloud Role: Reserved H100/H200 instances with dedicated capacity
• Multimodal (LLM + video + image + TTS) — Primary Provider: GMI Cloud — GMI Cloud Role: 100+ models across 5 categories on single API

FAQ

Q: Do custom-silicon providers (Cerebras, Groq) always beat GPU-based providers on latency?

For supported models, yes, they typically deliver lower TTFT and higher tokens/second. But their model catalogs are narrower, and capacity can be constrained.

GPU-based providers on H100/H200 offer broader model support and more predictable availability, with latency that's competitive for most production use cases.

Q: How does GMI Cloud's API latency compare to specialized providers?

GMI Cloud's Deploy endpoints on H100/H200 with pre-configured TensorRT-LLM deliver production-grade latency competitive with GPU-based providers like Fireworks.

Custom-silicon providers (Groq, Cerebras) can be faster on supported models, but GMI Cloud offers broader model coverage (100+ models) and owned GPU infrastructure with no capacity contention.

Q: Should I use one provider or multiple for agent inference?

Multiple. Route latency-critical calls to speed-specialized providers and cost-sensitive bulk calls to budget-efficient options.

GMI Cloud works as both: Deploy endpoints for latency-sensitive workloads, Batch mode for bulk processing, and the Model Library API (GLM-5 at $3.20/M, GLM-4.7-Flash at $0.40/M) for cost-optimized volume. Check console.gmicloud.ai for pricing.

Q: What's the single most important hardware spec for low-latency inference?

Memory bandwidth. Token generation speed is bounded by how fast the GPU reads model weights from VRAM. H200 at 4.8 TB/s generates tokens ~43% faster than H100 at 3.35 TB/s for bandwidth-bound workloads, at only a ~19% price premium ($2.50 vs $2.10/GPU-hour on GMI Cloud).

Which AI Inference Platform Offers