Data as of Aug 25, 2026 · Based on 38 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
TensorRT-LLMYour brand can be here too.
Source | Citation share |
|---|---|
 2025 Guide to Choosing an LLM Inference Provider | GMI Cloudhttps://www.gmicloud.ai/en/blog/choosing-a-low-latency-llm-inference-provider-2026 | 3.2% |
 Comparative Analysis of Large Language Model Inference Serving Systems: A Performance Study of vLLM and HuggingFace TGIhttps://arxiv.org/abs/2511.17593 | 1.9% |
 Best LLM API Providers in 2026: We Reviewed 8 Optionshttps://fireworks.ai/blog/best-llm-api-providers | 1.9% |
 The Best Open-Source LLMs in 2026https://www.bentoml.com/blog/navigating-the-world-of-open-source-large-language-models | 1.6% |
vLLM vs SGLang vs TensorRT-LLM: Picking an LLM Serving Enginehttps://aitechconnect.in/news/vllm-vs-sglang-vs-tensorrt-llm-serving-engine-2026 | 1.3% |
Best Open Source LLMs in 2026: We Reviewed 7 Modelshttps://fireworks.ai/blog/best-open-source-llms | 1.3% |
Comparing API Providers for Hosted Open-Source LLMs | Mediumhttps://hammansamuel.medium.com/comparing-api-providers-for-hosted-open-source-llms-3a5b2c9982fe | 1.3% |
Best Model Serving Platforms in 2026 | DeployBasehttps://deploybase.ai/articles/best-model-serving-platforms-in-2026 | 1.1% |
LLM Serving Frameworks Ranked 2026: vLLM, SGLang, TGI, TensorRT-LLM | DeployBasehttps://deploybase.ai/articles/llm-serving-framework | 1.1% |
 Bench Serving Guide — SGLanghttps://docs.sglang.ai/developer_guide/bench_serving | 1.1% |
When it comes to serving open-source Large Language Models (LLMs) with low latency and high throughput, there is no single "best" platform —instead, it depends on whether you are self-hosting an inference engine or using a managed cloud API provider.
The leading choices break down across self-hosted open-source inference engines and managed serverless platforms.
Phase 1: Self-Hosted Inference Engines (Maximum Performance & Control)
If you manage your own GPUs (like NVIDIA H100s or A100s), open-source inference runtimes utilize advanced techniques like PagedAttention, continuous batching, and kernel fusion to slash Time-to-First-Token (TTFT) and maximize tokens-per-second.
- **Best for:** Multi-turn conversations, agentic workflows, and structured generation.
- **Why it wins on latency:** SGLang features a novel runtime and programming language that optimizes RadixAttention (prefix caching). By automatically caching KV (Key-Value) states across multi-turn prompts or system instructions, it drastically cuts down redundant computation for interactive chat apps.[](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA) [[1]](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA)[[2]](https://google.com/goto?url=CAESjQEB6zswFf_WKYAz1cKg1Xn_uYTIa-KEgqMJQgkrsAaU-m5C7pOGj-UNdUHgXxjOZGNTUvdl7RJSdksQpeFWCYKapixHKP0OiXBJBl_xi3qEgSoVtNQzmXUS3PCd3DuBBcqYzxzYnjtXg46Kqk-RACm6Bnigra5P6M0kkkkfeN5nTMYbNiwf4U04Kuz06QI)
- **Best for:** Raw throughput and optimized quantized model serving (e.g., INT4/INT8).
- **Why it wins on latency:** Developed by the OpenMMLab team, LMDeploy often matches or exceeds SGLang in raw token generation speed on NVIDIA hardware through heavily tuned CUDA kernels, making it hyper-efficient for heavy production loads.[](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA) [[1]](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA)
- **Best for:** General production use, broad model compatibility, and rich ecosystem integration.
- **Why it wins on latency:** [vLLM](https://google.com/goto?url=CAESRwHrOzAVcQmwj_gIY_bNHOzUBOPJh2KIHw_oS1GPRsUDJUeo6Ks6CriTMdo-rDao0XFM0-CzgF85Gdu1dLSkcIY9UIQvVqgY) pioneered PagedAttention, which eliminates memory fragmentation in GPU RAM. While slightly behind SGLang and LMDeploy in absolute raw speed benchmarks for specific niche workloads, it remains the gold standard for stability, ease of deployment, and out-of-the-box support for newly released open-weight architectures.[](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA) [[1]](https://google.com/goto?url=CAESfgHrOzAVzovK2yQXrixZQOppZ7RXgnBy3lmKOUnZmG5vHIDtgMJlKeGldIrFFsWqKMFcH7AiBo5isL0xOlxit66CZHpjU-7z19RlbCQAJU_DpNGabOwYhPEaWWzsU-c-DHAU3EBm84KIe6Arwm6iDpczSGa5dv1TngD612sHWA)[[2]](https://google.com/goto?url=CAESjQEB6zswFf_WKYAz1cKg1Xn_uYTIa-KEgqMJQgkrsAaU-m5C7pOGj-UNdUHgXxjOZGNTUvdl7RJSdksQpeFWCYKapixHKP0OiXBJBl_xi3qEgSoVtNQzmXUS3PCd3DuBBcqYzxzYnjtXg46Kqk-RACm6Bnigra5P6M0kkkkfeN5nTMYbNiwf4U04Kuz06QI)
- **Best for:** Enterprise deployments locked into NVIDIA infrastructure.
- **Why it wins on latency:** It packages TensorRT-LLM optimizations into a clean microservice container, providing ultra-low latency via low-level GPU hardware instructions without requiring manual compilation.[](https://google.com/goto?url=CAESjQEB6zswFf_WKYAz1cKg1Xn_uYTIa-KEgqMJQgkrsAaU-m5C7pOGj-UNdUHgXxjOZGNTUvdl7RJSdksQpeFWCYKapixHKP0OiXBJBl_xi3qEgSoVtNQzmXUS3PCd3DuBBcqYzxzYnjtXg46Kqk-RACm6Bnigra5P6M0kkkkfeN5nTMYbNiwf4U04Kuz06QI) [[1]](https://google.com/goto?url=CAESjQEB6zswFf_WKYAz1cKg1Xn_uYTIa-KEgqMJQgkrsAaU-m5C7pOGj-UNdUHgXxjOZGNTUvdl7RJSdksQpeFWCYKapixHKP0OiXBJBl_xi3qEgSoVtNQzmXUS3PCd3DuBBcqYzxzYnjtXg46Kqk-RACm6Bnigra5P6M0kkkkfeN5nTMYbNiwf4U04Kuz06QI)[[2]](https://google.com/goto?url=CAESTQHrOzAVMbS38qKkCOjjhp6geWfVtl0R2_f6PN2VhHxbANgG8RdCzP4ojidpH7pkaK1jqtXmLuXZroPNuVJbTXwkJ5o8jP6HqM3JGn_i)
Phase 2: Managed Cloud Platforms (Zero Infrastructure Overhead)
If you prefer not to manage bare-metal GPU clusters, specialized managed serverless providers offer optimized open-source model inference with global low-latency routing:
Summary Recommendation
To help narrow this down, let me know:
If low latency is the priority, my pick in 2026 is Fireworks AI for a managed service, and SGLang/vLLM on dedicated GPUs if you want to control the infrastructure.
| Platform | Best for | Latency | Ease | Control |
|---|---|---|---|---|
| Fireworks AI | Lowest-latency managed inference | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| SGLang | Maximum performance on your own GPUs | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| vLLM | Best all-around self-hosted option | ⭐⭐⭐⭐½ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Together AI | Broad model catalog + easy API | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| Baseten | Custom models + production infrastructure | ⭐⭐⭐⭐½ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
Fireworks AI is particularly compelling if you don't want to operate GPUs: its serving stack is heavily optimized for inference, and it advertises industry-leading latency and high throughput across open models.
For self-hosting, I'd start with vLLM. It's extremely mature, supports an OpenAI-compatible API, has broad model/hardware support, and now has explicit interactivity performance settings for favoring per-request latency over throughput.
SGLang is worth benchmarking against vLLM if your workload has lots of shared prefixes, long system prompts, multi-turn conversations, or agentic traffic. Its caching/attention optimizations can make it particularly attractive for those workloads.
One important caveat: there isn't a universal latency winner. TTFT, tokens/sec, p95 latency, concurrency, model size, quantization, context length, and GPU type can completely change the ranking. For a real production decision, I'd benchmark your exact model and traffic pattern rather than rely on provider-wide claims.
If you tell me which model (e.g. Qwen, Llama, DeepSeek), expected requests/sec, and whether you're using GPUs or managed APIs, I can narrow this down to the best architecture and likely cheapest/fastest option.
If by “platform” you mean the serving layer for self-hosted open-weight LLMs, my default pick in 2026 is vLLM. But if lowest possible latency is the primary objective, I’d seriously benchmark SGLang and TensorRT-LLM against it.
| Platform | Best for | Latency | Ease | My take |
|---|---|---|---|---|
| SGLang | Agents, RAG, repeated prompts | 🥇 | High | Best latency-oriented default |
| TensorRT-LLM | NVIDIA-only, maximum performance | 🥇 | Lower | Best for squeezing NVIDIA GPUs |
| vLLM | General production serving | 🥈 | 🥇 | Best overall/default |
| llama.cpp | CPU/single-GPU/local deployment | Good | 🥇 | Great for smaller models |
| TGI | Existing Hugging Face deployments | — | — | Less attractive for new deployments |
Recent 2026 comparisons consistently put SGLang, vLLM, and TensorRT-LLM at the top. SGLang's advantage is particularly relevant when requests share long prefixes—common with agents, multi-turn conversations, and RAG—because of its RadixAttention/prefix caching.
For a concrete H100 comparison, one recent same-hardware test reported roughly 112 ms p50 TTFT for SGLang, 120 ms for vLLM, and 105 ms for TensorRT-LLM under its particular workload. TensorRT-LLM, however, had substantially greater setup/compilation overhead.
One important distinction: serving engine ≠ hosting platform. If you're asking “Where should I deploy my open-source model?” rather than “Which inference engine should I run?”, the answer changes—I’d compare Fireworks, Together, Groq, Modal, RunPod, and Baseten based on latency, GPU availability, and cost.
If you tell me the model (e.g. Qwen, Llama, DeepSeek), GPU (H100/H200/B200/etc.), and expected requests/sec, I can give you a much more specific recommendation.
When looking for the best platform or engine to serve open-source LLMs with ultra-low latency, the ideal choice depends on whether you are self-hosting (managing your own infrastructure) or looking for a managed API provider.
Phase 1: Self-Hosted Inference Engines (If you manage your own GPUs)
If you are deploying models onto your own GPU cluster, standard PyTorch is far too slow. You need an optimized inference engine that uses techniques like PagedAttention, continuous batching, and kernel fusion to slash time-to-first-token (TTFT) and maximize throughput.
(Note: Hugging Face TGI / Text Generation Inference was widely used, but it was moved to maintenance mode and archived, so new projects should avoid it.)
Phase 2: Fully Managed Cloud APIs (If you want zero infrastructure overhead)
If you prefer not to manage servers, container orchestration, or GPU scaling, dedicated third-party hosting platforms deliver optimized open-weight inference via APIs:
To help narrow down the exact right path, tell me:
The "best" platform for serving open-source LLMs with low latency depends entirely on whether you want to self-host on your own infrastructure or use a managed API/cloud provider.
Phase 1: Self-Hosted & Open-Source Serving Frameworks (If you manage the GPUs)
If you are deploying weights onto your own cloud cluster or local hardware, the choice comes down to throughput vs. time-to-first-token (TTFT) latency.
llama.cpp often delivers exceptional token generation speeds and fast local setups on smaller configurations.Phase 2: Managed API & Inference Providers (If you want zero infrastructure overhead)
If you prefer an API approach where a third party handles the hardware optimization and delivers raw speed, these are the leading options:
To help narrow down the ideal choice, let me know:
If you mean a platform/API for deploying open-weight LLMs with the lowest practical latency, my pick today is Together AI for a managed service, and SGLang if you want to run the serving stack yourself.
| Platform | Best for | Latency | Ops burden |
|---|---|---|---|
| Together AI | Managed production inference | ⭐⭐⭐⭐⭐ | Very low |
| SGLang | Maximum control/performance | ⭐⭐⭐⭐⭐ | High |
| vLLM | General-purpose self-hosting | ⭐⭐⭐⭐½ | Medium-high |
| TensorRT-LLM | NVIDIA-specific optimization | ⭐⭐⭐⭐⭐ | High |
| Groq | Extreme latency, supported models | ⭐⭐⭐⭐⭐ | Very low |
Together AI is probably the best default if you don't want to manage GPUs. It offers serverless inference for 100+ open-source models and dedicated endpoints for predictable latency. Together currently claims up to 2.75× faster inference than competing providers on some models, with optimizations including speculative decoding and custom inference kernels.
If you're self-hosting, I'd choose SGLang over vLLM for a latency-first workload. SGLang specifically targets low-latency/high-throughput serving and includes RadixAttention, prefix caching, speculative decoding, and multi-GPU optimizations.
vLLM remains an excellent choice when ecosystem maturity and broad model compatibility matter more than squeezing out the last bit of latency. It also has production scaling through Ray Serve LLM, including autoscaling, load balancing, and multi-node deployment.
One important caveat: TTFT vs. tokens/sec vs. end-to-end latency can produce different winners, and model size, context length, concurrency, GPU, and quantization matter enormously. SGLang's own benchmarking tooling measures TTFT, inter-token latency, throughput, and end-to-end latency separately for exactly this reason.
If you tell me which model (e.g. Llama, Qwen, DeepSeek), expected requests/sec, and target latency, I can give you a much more specific recommendation and estimated GPU configuration.
When it comes to serving open-source Large Language Models (LLMs) with low latency, there is no single "best" platform , but rather a clear winner depending on your infrastructure, team size, and traffic pattern. The landscape is dominated by three major high-performance open-source engines:
pip install and an OpenAI-compliant server. It provides the best balance of fast Time-to-First-Token (TTFT), ease of deployment, and broad hardware compatibility (NVIDIA, AMD, CPUs).If you want to narrow down the right choice, tell me:
If by “platform” you mean the inference/serving stack, my default choice today is SGLang for the lowest-latency production serving of open-source LLMs.
| Platform | Best for | Latency | Ease | Ecosystem |
|---|---|---|---|---|
| SGLang | Lowest latency / high-performance production | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| vLLM | Best all-around choice | ⭐⭐⭐⭐½ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| TensorRT-LLM | Maximum NVIDIA optimization | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| llama.cpp | Small models / CPU / edge | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
Why SGLang: it is specifically optimized around low-latency serving, with RadixAttention/prefix caching, continuous batching, speculative decoding, optimized kernels, and prefill/decode disaggregation. It supports NVIDIA and AMD GPUs as well as several other accelerators.
Why vLLM is still my default recommendation: it has an exceptionally broad model/hardware ecosystem, OpenAI-compatible APIs, PagedAttention, continuous batching, prefix caching, speculative decoding, quantization, and distributed-parallelism support.
One important caveat: the GPU, model architecture, quantization, context length, concurrency, and prompt/output lengths can matter more than the serving framework. SGLang itself provides benchmarking for TTFT, inter-token latency, end-to-end latency, and throughput, so I'd benchmark your actual workload rather than rely on generic framework rankings.
If you tell me which model (e.g. Qwen3, Llama 4, DeepSeek, GPT-OSS), GPU(s), and target concurrency, I can recommend the exact stack and configuration for minimum latency.
When looking for the best platform to serve open-source LLMs with low latency, the choice depends on whether you want to self-host and tune an open-source inference engine or use a managed API provider that abstracts the infrastructure away.
Part 1: Self-Hosted Open-Source Inference Engines (If managing your own GPUs)
If you are deploying weights onto your own infrastructure (NVIDIA H100s, Blackwell, etc.), three primary engines dominate production environments.
Part 2: Managed API & Cloud Inference Providers (No infrastructure management)
If you prefer an API where open-source models are already optimized for ultra-low latency out of the box:
To help narrow down the ideal stack, tell me:
If you mean self-hosting open-weight LLMs, my default choice in 2026 is SGLang, with vLLM as the safest general-purpose alternative.
| Platform | Best for | Latency | Throughput | Ease | My take |
|---|---|---|---|---|---|
| SGLang | Low-latency production serving | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Best overall for performance |
| vLLM | General-purpose production | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Best default |
| TensorRT-LLM | NVIDIA-only, maximum optimization | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | Best if you're all-in on NVIDIA |
| llama.cpp | Local/edge/CPU inference | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Best for small deployments |
SGLang is specifically designed around high-performance serving and includes optimizations such as RadixAttention/prefix caching, speculative decoding, disaggregated prefill/decode, and optimized scheduling. Its documentation explicitly targets low-latency, high-throughput production serving.
It's particularly attractive if your workload has:
There is also recent evidence of SGLang outperforming vLLM on particular workloads, although benchmark results are highly model-, GPU-, and workload-dependent.
I'd choose vLLM if you want the broadest ecosystem, easy deployment, OpenAI-compatible APIs, and excellent performance without spending as much time tuning the serving stack. It also provides dedicated latency and online-serving benchmarks, which makes it relatively straightforward to test against your actual workload.
A good architecture is:
Client → API gateway/router → vLLM/SGLang → GPU
rather than treating the inference engine itself as your entire serving platform.
If you're running NVIDIA GPUs exclusively and willing to invest engineering time, TensorRT-LLM can be the performance-maximizing option. NVIDIA provides specialized kernels and optimizations including FP8/NVFP4, speculative decoding, disaggregated serving, and expert parallelism.
The tradeoff is considerably more operational complexity and less portability.
One important caveat: "lowest latency" isn't a property of the platform alone. TTFT, inter-token latency, and p95/p99 end-to-end latency can change dramatically with prompt length, output length, concurrency, quantization, GPU, batching strategy, and model architecture. SGLang's benchmarking tooling, for example, explicitly measures TTFT, inter-token latency, end-to-end latency, and throughput.
If you tell me which model (e.g. Qwen3, Llama 4, DeepSeek, etc.), GPU (H100/H200/B200/A100), and target concurrency, I can give you a much more specific recommendation and deployment configuration.