Open-source alternatives to Groq: 5 low-latency LLM inference tools compared

What Groq's own silicon means for every candidate on this page

Groq is a hosted inference service built on its own inference hardware, and the speed of that hardware is what the service sells. Its catalogue is the vendor's choice, so a model that is not listed cannot be served in the same way, and requests are handled on the vendor's servers, so every prompt leaves your network.

Read that carefully and no open-source project on this page replaces it on latency. There is no equivalent you can run on your own machines. ggml-org/llama.cpp will not turn a general GPU into inference silicon. vllm-project/vllm and sgl-project/sglang extract more work per accelerator, and InternLM/lmdeploy fits more model into a fixed amount of memory, but a dedicated stack still has the edge on time to first token.

So the five candidates answer a different set of reasons for looking elsewhere. Three of the common reasons survive the move: the prompts and the documents inside them stop leaving your network, the catalogue becomes whatever you decide to run, and the hardware becomes a line item you control rather than a shared facility. For those three reasons the list below is a genuine replacement. For the reason of speed alone, it is not, and a reader whose only complaint is latency should keep using Groq.

ray-project/ray sits apart from the other four anyway. It is not an inference engine but a distributed runtime, so it belongs on the list as the place where an inference service gets scheduled, not as something that answers a request on its own.

llama.cpp for a single binary, vLLM and SGLang for a served endpoint

ggml-org/llama.cpp is the oldest design in the list and the smallest. It is plain C and C++ with no dependencies, built on the ggml library, and it ships a command line tool and a server. The server front end is OpenAI-compatible, the project ships a built-in web interface against it, and GBNF grammars give you constrained output. Quantization runs from 1.5-bit up to 8-bit, and the hybrid CPU and GPU mode partially accelerates a model larger than the memory of the card. The backend table covers CUDA, HIP, Metal, Vulkan, SYCL, CANN and more, with Hexagon and OpenVINO still marked as in progress. This is the shape you want when one machine and one binary have to do the job.

vllm-project/vllm is the general serving engine. Its mechanism is memory management and scheduling: paged attention for the key and value cache, continuous batching of arriving requests, chunked prefill, prefix caching, and piecewise or full CUDA and HIP graphs for execution. On top of that sit quantization in FP8, INT8, INT4, GPTQ, AWQ, GGUF and other formats, speculative decoding in several flavours, disaggregated prefill, decode and encode, and parallelism at tensor, pipeline, data, expert and context level. The hardware list is long, from NVIDIA, AMD and Intel accelerators through CPU-only machines to plugins for TPU, Gaudi, Ascend and Apple Silicon. The server speaks OpenAI, Anthropic Messages and gRPC.

sgl-project/sglang takes the same shape and aims it at agentic work and rollouts. It is Python and Rust, its release notes describe a zero-overhead batch scheduler and a cache-aware load balancer, and it adds expert parallelism for large mixture-of-experts models, day-0 support for new model releases, and a JAX backend for TPU hosts. The three engines differ less in raw capability than in what they optimise for.

LMDeploy and Ray: a tuned engine and a general runtime, two different bets

InternLM/lmdeploy comes from the InternLM team and does three jobs in one toolkit: compressing a model, deploying it and serving it. The TurboMind engine is the performance path, a C++ implementation with persistent batching, KV cache quantization, paged attention and W4A16 weight-only quantization, and it added MXFP4 support from the V100 generation onwards. A PyTorch engine written entirely in Python sits beside it for experimentation, and the project supports Huawei Ascend with graph mode execution. It also documents DeepSeek disaggregated prefill and decode through external projects, and a proxy server for multi-model, multi-machine services, and it is integrated as the default accelerator for vision-language models in another training framework. That makes it the most interesting candidate when the question is how much model fits in the memory you have.

ray-project/ray bets on something else. It is a distributed runtime for Python with two layers: a core built on tasks, actors and objects, and five libraries on top of it, covering datasets, distributed training, hyperparameter tuning, reinforcement learning and serving. The same code runs on a laptop and on a cluster, on Kubernetes or on a cloud provider, and there is a dashboard and a distributed debugger for the two things that go wrong in distributed systems, which are placement and failure. The library list is also the warning. Data, Train, Tune, RLlib and Serve are different products with different failure modes, and picking the wrong one is easy. The install is a single command, and the repository behind it is a Bazel monorepo spanning Python, C++ and Java.

The practical difference: LMDeploy is a serving engine you point a client at, while Ray is a substrate you build your serving layer on. That makes Ray a poor answer for one endpoint and a good answer for an organisation with several teams.

What hosting your own endpoint costs: builds, backends and one more service to watch

A hosted service gives you a process that is already running. Self-hosting gives you the process plus everything around it, and the candidates differ in how much of the surrounding work they hand back.

vllm-project/vllm hands back the most in exchange for a hard build. Our analysis of vllm records that the build requirements pin torch to an exact version and cap setuptools below a fixed release, so the toolchain in your image has to line up with the one the project compiles against. The version string comes from a git describe filtered to v[0-9]*, which means a shallow or untagged checkout has nothing to derive and a source build needs cmake, ninja, setuptools-rust and a Rust toolchain. The wheel path is one uv command; the source path is a project of its own.

ggml-org/llama.cpp is cheaper to start. Pre-built binaries exist for the common platforms, and the build guide covers the rest. The cost moves to configuration: the backend has to match your hardware, the hybrid CPU and GPU path has to be tuned for the model you load, and because the project ships releases quickly, a pinned release tag is the difference between a reproducible setup and one that changes underneath you.

sgl-project/sglang installs by pulling an image that pins nothing, and its 0.5.x line shipped three tags in four weeks. Its analysis says to pin the exact version or image digest, to check whether your hardware sits on the shipping list rather than the in-progress one, and to decide which KV cache project runs beside it. InternLM/lmdeploy adds its own packaging wrinkle: the project records that PyPI wheel uploads were paused and resumed in 2026-04, and the supported model list is fixed.

ray-project/ray costs more in cluster operations than in installation. Beyond the Python version requirement, you are running a head node, workers, a dashboard and a placement system, and you are the one deciding where a model lands. Across all five, two costs are shared: the weights still come from a model hub, so a private deployment means mirroring them, and the latency work that a dedicated inference stack does for you becomes an internal project with its own backlog.

Choosing by scenario, and where Cerebras readers land

A developer on a laptop starts with ggml-org/llama.cpp, because one binary, a local server and a GGUF conversion cover the whole job, and the cost of being wrong is a rerun rather than an outage. A small team with a GPU server picks vllm-project/vllm, because the OpenAI-compatible server and the batching behaviour are what they need, and the pin discipline in the build files is a cost they can absorb.

A team serving agentic traffic or running rollouts reads the SGLang section and confirms the KV cache project and the RL trainer, because those pieces live elsewhere. A shop with Ascend hardware, or one whose main problem is fitting a large model into a fixed budget of memory, reads the LMDeploy section and measures on its own workload instead of trusting the single published comparison. An organisation with several teams that each need training, tuning and serving picks ray-project/ray and accepts that it gives them a substrate rather than an endpoint.

A company with compliance requirements gets the residency answer, and should price the other side of it honestly. Prompts and documents stop leaving the network, the catalogue is yours, and the hardware is yours. In exchange you own the latency engineering that a dedicated inference stack was doing for you, and self-hosted throughput on general accelerators is usually worse per request than the hosted service was.

The same shortlist answers the Cerebras question, because Cerebras also sells its own silicon, and the same warning applies to it. Where each candidate is the weaker choice is specific. Ray is wrong when you need one endpoint. llama.cpp is wrong when you need fleet-level scheduling. LMDeploy is wrong when your model is not on its list. SGLang is wrong for a single machine, and vLLM is wrong when the missing piece is training.

Bottom line

Take ggml-org/llama.cpp for a single machine, vllm-project/vllm for a served endpoint on your own accelerators, sgl-project/sglang for agentic or rollout traffic, InternLM/lmdeploy for Ascend or quantized deployments, and ray-project/ray only when several teams need one runtime. Before committing, measure time to first token on your own hardware, confirm the model you need exists in the format the engine expects, and read the licence attached to the weights you plan to serve.

Sources

1. Groq official site
2. vllm-project/vllm repository
3. vllm-project/vllm README
4. sgl-project/sglang repository
5. sgl-project/sglang README
6. ggml-org/llama.cpp repository
7. ggml-org/llama.cpp README
8. InternLM/lmdeploy repository
9. InternLM/lmdeploy README
10. ray-project/ray repository
11. ray-project/ray README