What's the best platform for serving open-source LLMs with low latency?

When it comes to serving open-source Large Language Models (LLMs) with ultra-low latency, the "best" platform depends on whether you want to self-host and manage your own inference engine or use a fully managed cloud API provider.

Category 1: Self-Hosted Inference Engines (Open-Source Frameworks)

If you are deploying on your own GPU infrastructure (like NVIDIA H100s/A100s), these are the leading open-source engines optimized for maximum throughput and minimal Time-to-First-Token (TTFT):

• SGLang: Known for lightning-fast execution in multi-turn conversations and complex prompting structures. It features RadixAttention to reuse KV cache across requests, drastically cutting down latency for agentic workflows and chat apps.
• LMDeploy: Developed by the OpenMMLab team, it dominates in high-performance serving of quantized models (like INT4/INT8) using TurboMind, offering exceptional token generation speeds on NVIDIA hardware.
• vLLM: The industry gold standard for ecosystem maturity. While its raw generation throughput can trail SGLang or LMDeploy by a minor margin, it has the widest hardware support (NVIDIA, AMD ROCm, Google TPUs, AWS Neuron) and seamless integration tools.
• TensorRT-LLM: NVIDIA’s heavily optimized, proprietary-backend library. It yields incredible low-latency performance if you are locked strictly into NVIDIA hardware and have the engineering bandwidth to compile models natively.

Category 2: Managed Cloud & Specialized Hardware Providers

If you prefer not to manage bare-metal GPU clusters and want sub-200ms latency out-of-the-box via an API:

• Groq: Unmatched for pure, raw, sub-millisecond per-token latency. Groq uses custom LPU (Language Processing Unit) hardware rather than traditional GPUs, making it the premier choice for real-time interactive voice bots or instant-streaming UI responses.
• Fireworks AI: Excels at blazing-fast median output speeds for open-source weights using heavily optimized custom serving infrastructure.
• SiliconFlow / Together AI / DeepInfra: Highly cost-effective managed cloud platforms that deploy optimized inference backends (often customized vLLM or SGLang setups) to deliver low-latency inference at scale.

To help narrow down the ideal choice, let me know:

• Are you looking to self-host on your own GPUs or use a managed API?
• What hardware (e.g., NVIDIA H100, RTX series, or custom accelerators) are you targeting?
• What is your primary workload (high-concurrent chat, batch processing, or real-time voice)?

For low-latency open-source LLM serving, I’d start with SGLang. It is specifically optimized for low latency and high throughput, with RadixAttention/prefix caching, continuous batching, speculative decoding, and strong multi-GPU support.

• SGLang: particularly compelling for agentic workloads and repeated/shared prompts.
• vLLM: best general-purpose default; extremely mature, broad model/hardware support, and excellent throughput.
• TensorRT-LLM: worth benchmarking when you're standardized on NVIDIA hardware and chasing maximum performance.

If you tell me GPU (H100/A100/4090/etc.), model, and target concurrency, I can recommend the specific stack/configuration.

Groq is the best platform for absolute ultra-low latency when serving open-source LLMs due to its custom LPU (Language Processing Unit) architecture, while vLLM run on high-performance cloud GPUs via Fireworks AI, Together AI , or Baseten offers the best combination of low latency, high throughput, and model flexibility for standard hardware.

The optimal choice depends on whether you prefer a specialized hardware provider, a managed serverless API, or an open-source self-hosted engine.

Top Platforms for Low-Latency Open-Source LLM Serving

• Groq: Ultra-low latency inference using custom LPU chips rather than traditional GPUs, making it unmatched for real-time conversational responses on supported models.
• Fireworks AI: Top-tier inference performance on standard NVIDIA hardware utilizing optimized serving runtimes, competitive pricing, and fast Time-to-First-Token (TTFT).
• Together AI: High throughput and speed with optimized open-source model execution, flexible API endpoints, and a wide selection of community and frontier models.
• Baseten: Enterprise-grade production serving that provides dedicated and serverless deployments with custom tracing, quantization, and minimal latency overhead.
• vLLM (Self-Hosted): Maximum infrastructure control when deployed on GPU clouds like CoreWeave or RunPod , usingPagedAttention to dramatically increase throughput and lower latency locally.

Platform Comparison

If you mean self-hosting open-weight LLMs, my default pick in 2026 is SGLang for the lowest latency, with vLLM as the safer all-around choice.

My ranking

SGLang is particularly compelling when your workload has repeated system prompts, long shared prefixes, or high concurrency. Its RadixAttention/prefix caching, continuous batching, speculative decoding, and prefill/decode disaggregation are explicitly designed around low-latency serving.

vLLM is what I'd choose if you want the least operational friction. It has an OpenAI-compatible API, broad Hugging Face compatibility, PagedAttention, continuous batching, prefix caching, quantization, speculative decoding, and distributed serving.

TensorRT-LLM can be the performance winner if you're standardized on NVIDIA GPUs and willing to optimize around NVIDIA's stack. It supports in-flight batching, paged KV caching, FP8/FP4/INT4 quantization, speculative decoding, and multi-GPU/multi-node inference.

What I'd pick

• Fastest interactive chatbot / agent: SGLang
• Production API with minimal fuss: vLLM
• NVIDIA Blackwell/Hopper and absolute maximum performance: TensorRT-LLM
• Local workstation / CPU / edge device: llama.cpp

One important caveat: "lowest latency" depends heavily on the model, GPU, prompt length, output length, concurrency, quantization, and whether you care about TTFT or tokens/sec. A benchmark on your exact workload is more meaningful than a generic framework ranking.

If you tell me which model (e.g. Qwen3, Llama 4, DeepSeek), GPU(s), and target concurrency, I can recommend the exact serving stack and configuration.

TensorRT-LLM can be the performance winner if you're standardized on NVIDIA GPUs and willing to optimize around NVIDIA's stack. It supports in-flight batching, paged KV caching, FP8/FP4/INT4 quantization, speculative decoding, and multi-GPU/multi-node inference.

What I'd pick

For serving open-source LLMs with low latency, the best platform depends on what kind of latency you mean:

• Lowest time-to-first-token (TTFT) for interactive apps: usually SGLang or TensorRT-LLM (especially on NVIDIA GPUs)
• Best overall production default: vLLM
• Simplest self-hosting: Ollama (but not usually the choice for high-QPS production)
• Edge / CPU inference: llama.cpp

SandBase Blog Genαi## My shortlist

Awesome Agents LLMOps Report## Recommendation by workload

1. Chatbot / API serving (most companies)

Pick vLLM.

Why:

• Strong throughput under concurrency
• Mature ecosystem
• OpenAI-compatible API
• Good GPU utilization through paged KV cache management

A typical stack:

Client
  ↓
API gateway
  ↓
vLLM server
  ↓
A100/H100/L40S GPUs

Data-Gate### 2. AI agents, RAG, long system prompts

Pick SGLang.

It can win when many requests share prefixes, such as:

• large system prompts
• retrieved documents
• multi-turn conversations
• tool-use agents

The KV-cache reuse can reduce repeated prompt computation and improve latency.

3. Absolute minimum latency on NVIDIA

Pick TensorRT-LLM.

Good fit if:

• you control the hardware
• you only need a few models
• you can tolerate more deployment complexity

It tends to require more engineering than vLLM/SGLang.

4. Small models locally

Pick llama.cpp or Ollama.

Examples:

• laptop assistants
• offline apps
• embedded devices
• demos

They trade some throughput for simplicity.

If I were building a new production service today

I would start with:

1. vLLM as the default serving layer
2. Benchmark SGLang if: - prompts are repetitive

• you have agents/RAG
• TTFT matters more than raw throughput

3. Move to TensorRT-LLM only if latency targets justify the operational cost

The biggest mistake is choosing based only on benchmark numbers. Measure your own:

• p50/p95 TTFT
• tokens/sec/user
• concurrent users
• prompt length distribution
• GPU cost per million tokens

The "fastest" server on a benchmark is often not the fastest for your traffic pattern.

When looking for the best platform to serve open-source LLMs with ultra-low latency, the "best" choice depends on whether you want a managed API/Inference Provider (plug-and-play) or a self-hosted inference engine (maximum control and hardware optimization).

Phase 1: High-Performance Managed API Providers (No Infrastructure Management)

If you want to call an API with minimal Time to First Token (TTFT) without managing raw GPU clusters:

• • Groq: Unmatched for pure token generation speed and ultra-low latency via its proprietary LPU (Language Processing Unit) architecture. Best if your open-source models of choice (like Llama) are natively supported. Explore options on the Groq Console.
• • Fireworks AI: Renowned for extremely fast inference speeds, custom fine-tunes, and high throughput using optimized packing and quantization techniques. Check details at Fireworks AI.
• • Together AI: Provides high-performance serverless and dedicated endpoints for a massive catalog of open-source models with low latency and competitive pricing structures. Visit Together AI.
• • Baseten / Modal: Ideal if you want serverless flexibility combined with dedicated GPU control. They let you deploy custom vLLM containers on infrastructure like H100s with pay-as-you-go efficiency. Check out Baseten or Modal.

Phase 2: Best Self-Hosted Inference Engines (Maximum Performance & Control)

If you are deploying on your own cloud infrastructure (e.g., AWS, Lambda Labs, CoreWeave) using H100/H200 GPUs, the underlying software runtime dictates your latency:

1. • vLLM: The gold standard for high-concurrency chat applications. It utilizes PagedAttention to maximize KV-cache memory efficiency and continuous batching, drastically reducing queuing delay and time-to-first-token under load.
2. • TensorRT-LLM (NVIDIA): The fastest raw execution engine if you are locked into high-end NVIDIA hardware (SXM H100/H200). By compiling models into optimized, hardware-specific graphs with native FP8 quantization, it achieves unbeatable raw latency—though it carries a steeper engineering and configuration curve.
3. • SGLang: Rapidly gaining popularity for complex multi-turn generation and agentic workflows, optimizing structured generation and radix attention for prompt caching.

Summary Recommendation

• For instant low-latency APIs: Go with Groq (for raw speed) or Fireworks AI (for broad model variety).
• For custom self-hosted scale: Deploy vLLM for easy configuration and robust concurrency, or TensorRT-LLM if you have specialized infrastructure engineers to squeeze maximum performance out of NVIDIA silicon.

To help narrow this down, let me know:

• Are you looking for a managed API or self-hosting on your own GPUs?
• What specific open-source model (e.g., Llama 3, DeepSeek, Qwen) and expected concurrency/traffic volume are you planning for?

When you talk about serving open-source Large Language Models (LLMs) with low latency , the "best" platform depends on whether you want to self-host (run the inference engine yourself on cloud GPUs) or use a managed API provider (send requests to an optimized cloud endpoint).

Phase 1: Self-Hosted Inference Engines (If you manage your own infrastructure)

If you are spinning up your own GPU instances (like AWS, RunPod, or Lambda Labs), the underlying serving framework dictates your latency—specifically minimizing Time to First Token (TTFT) and maximizing inter-token generation speed.

1. vLLM : The gold standard for general production. It introduces PagedAttention to manage KV cache memory with near-zero waste, allowing massive concurrent throughput and low latency. It also features a drop-in OpenAI-compatible server.
2. SGLang : Rapidly gaining ground on vLLM for state-of-the-art speed. It co-designs the backend runtime with a structured generation language, making it exceptionally fast for complex agentic workflows, multi-turn conversations, and structured JSON outputs.
3. TensorRT-LLM : Built by NVIDIA, this is typically the absolute raw-speed winner for uncompressed and quantized models on NVIDIA hardware. However, it has a steeper learning curve and requires custom building/compiling engine files for specific GPU architectures.
4. LMDeploy : Highly optimized for high decoding performance and efficient memory handling, particularly strong with continuous batching and various quantization weights on NVIDIA cards.

Phase 2: Managed Serverless APIs (If you prefer not to manage GPUs)

If you don't want to deal with cold starts, cluster orchestration, or manual scaling, specialized managed inference providers deliver extreme low-latency routing out of the box:

1. Groq : If absolute lowest latency (tokens per second) is your primary metric, Groq's custom LPU (Language Processing Unit) hardware architecture blows traditional GPUs out of the water for supported open models.
2. Fireworks AI : Known for lightning-fast inference speeds (utilizing optimized runtimes like FireAttention) and supporting a massive library of bleeding-edge open weights with minimal TTFT.
3. Modal : Excellent if you want serverless execution but want total control to deploy your own custom vLLM container scripts without provisioning persistent virtual machines.

Summary Recommendation

• Choose vLLM if you want the best balance of community support, ease of integration, and high-performance self-hosting.
• Choose SGLang if your app relies heavily on complex agent loops, multi-step prompting, or structured outputs.
• Choose Groq or Fireworks AI if you want to bypass self-hosting entirely and require sub-second response times instantly via API.

To help narrow down the ideal stack, tell me:

• Are you self-hosting on your own GPUs or looking for a managed API?
• Which open-source model (e.g., Llama 3/4, DeepSeek, Qwen) are you planning to serve?
• What is your expected concurrency (number of simultaneous users)?

If you mean self-hosting open-source LLMs in production, my default pick in 2026 is vLLM.

Quick ranking

Why vLLM: it combines very strong serving performance with PagedAttention, continuous batching, prefix caching, speculative decoding, quantization, distributed inference, and an OpenAI-compatible API. It also supports NVIDIA, AMD, and several other accelerator platforms.

For interactive low latency, vLLM now has an explicit interactivity performance mode that favors per-request latency at low batch sizes, rather than maximizing aggregate throughput.

SGLang is particularly compelling if your workload repeatedly sends the same long system prompts, RAG context, or agent prefixes—the caching architecture can make a substantial difference.

TensorRT-LLM is what I'd benchmark if you're running NVIDIA GPUs and latency is the overriding objective. NVIDIA specifically provides low-latency engine/benchmark workflows, and its optimization stack is tightly coupled to NVIDIA hardware.

My recommendation

• Most production deployments: vLLM
• Agent/RAG workload with lots of shared prefixes: SGLang
• NVIDIA Blackwell/Hopper + absolute minimum latency: TensorRT-LLM
• Single workstation / CPU / edge device: llama.cpp

One important distinction: serving engine ≠ hosting platform. If by "platform" you mean a managed cloud service where you deploy a model without operating GPUs, the answer changes considerably.

If you tell me which model (e.g. Qwen, Llama, DeepSeek), GPU (H100/H200/B200/A100), and target latency/concurrency, I can recommend a specific stack and configuration.

SGLang is particularly compelling if your workload repeatedly sends the same long system prompts, RAG context, or agent prefixes—the caching architecture can make a substantial difference.

For low-latency serving of open-source LLMs, the best platform depends on what you mean by “low latency” (single-user response time vs. high-concurrency throughput). The usual production answer is:

Best default: vLLM

Choose vLLM for most production deployments. It is optimized for GPU inference with features like PagedAttention, continuous batching, tensor parallelism, and an OpenAI-compatible API. It is usually the strongest balance of latency, throughput, model support, and operational maturity.

Best for:

• Customer-facing LLM APIs
• Multiple concurrent users
• RAG applications
• Chat systems
• Serving 7B–70B+ models on NVIDIA GPUs

Typical stack:

Client
  ↓
OpenAI-compatible API
  ↓
vLLM
  ↓
CUDA / GPUs
  ↓
Llama, Qwen, Mistral, DeepSeek, etc.

---

If absolute latency matters: SGLang

SGLang can outperform vLLM for workloads with lots of repeated prompts because of prefix caching approaches such as RadixAttention. It is especially attractive for:

• Agents
• Tool calling
• Long system prompts
• RAG with repeated context
• Structured JSON generation

Rahul Kashyap Data-Gate

If you want easiest deployment: Ollama

Great for:

• Local development
• Internal tools
• Single-user apps
• Mac/desktop deployments

Tradeoff: it is not usually the choice for maximizing requests per GPU in production.

---

If you need edge / CPU inference: llama.cpp

Best for:

• Running on laptops
• Apple Silicon
• Embedded devices
• CPU-only deployments
• Quantized GGUF models

Hosn

If you need maximum NVIDIA optimization: NVIDIA TensorRT-LLM

Best when:

• You have a fixed NVIDIA fleet
• You need every last token/sec
• You can invest in engine builds and tuning

Tradeoff: less flexible than vLLM when swapping models frequently.

---

Quick decision table

Awesome Agents Rahul KashyapFor a new production system today, I would start with vLLM, benchmark against SGLang using your real prompts, and only move to TensorRT-LLM if GPU cost at scale justifies the extra engineering.

If you share your model size (e.g., Llama 8B vs 70B), GPU type (A100/H100/L40S/consumer GPU), and expected concurrency, I can suggest a more specific stack.

For self-hosted, low-latency open-source LLM inference, vLLM, TensorRT-LLM , and SGLang represent the leading high-performance serving frameworks, while managed options like DeepInfra and Fireworks AI provide top-tier cloud speed without infrastructure overhead.

Core Recommendations

• Choose TensorRT-LLM if you are locked into NVIDIA hardware and require absolute maximum raw throughput and lowest latency via deep hardware compilation.
• Choose vLLM as the default production choice for an ideal balance of effortless setup, continuous batching, PagedAttention memory management, and broad hardware support (NVIDIA, AMD ROCm, AWS Trainium).
• Choose SGLang if your workload relies heavily on multi-turn system prompts or complex agent execution trees where prefix-caching significantly slashes latency.

Would you like to:

• Review a benchmark comparison of time-to-first-token (TTFT) vs tokens-per-second across these frameworks?
• Explore a docker-compose deployment guide for setting up vLLM with an OpenAI-compatible endpoint?