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Choose the runtime that matches your model, accelerator, and service target—not the one with the biggest headline number. Evaluate Liquid AI’s RIPL first when you are serving Liquid Foundation Models (LFMs) on supported NVIDIA GPUs and have a hard tail-latency budget. Choose vLLM when production flexibility, continuous batching, throughput, parallelism, and broad hardware support matter most. Choose TensorRT-LLM for an NVIDIA-centered optimization and serving workflow. Choose llama.cpp for local or embedded inference across varied operating systems and hardware, provided your model is available in a compatible format such as GGUF.
Liquid AI reports substantially lower p95 latency for two RIPL comparisons, but those tests use different model families and sizes on each runtime. They are not a controlled runtime-only benchmark. Before committing, test the exact model, GPU, quantization, prompt and output lengths, concurrency, and quality target you will deploy.
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What is Liquid AI RIPL, and who should evaluate it?
RIPL (Real-time Inference Pipeline) is Liquid AI’s enterprise inference engine for putting Liquid Foundation Models into a live request path. Liquid AI announced it on October 22, 2026, describing it as “a low-latency inference engine built to put Liquid Foundation Models (LFMs) directly in the path of a live request.” The announcement names NVIDIA H100, H200, B200, and RTX PRO 6000 GPUs as enterprise deployment targets. Read the launch description at Liquid AI’s RIPL announcement.
That positioning is narrower than “a faster replacement for every LLM server.” RIPL is most relevant when all three of these conditions hold:
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- Your model is a Liquid Foundation Model. Liquid’s model catalog is at Liquid Foundation Models.
- Your production accelerator is one of the NVIDIA targets supported by the current RIPL release and your exact model configuration is supported.
- Your user experience is constrained by tail latency—especially p95 or p99 response time—rather than only by tokens per second across a large batch.
Liquid AI says Shopify is deploying LFMs in production across latency-sensitive commerce workloads, with search enhancement given as an example. That is a statement from the vendor, not an independent performance certification.
RIPL’s complete installation procedure, version-specific model matrix, operational requirements, pricing, and terms are not established by the launch material cited here. Treat those as procurement and engineering checks to complete with Liquid AI before a production decision.
Is RIPL faster than vLLM?
Liquid AI reports lower p95 latency for RIPL in two workload comparisons, but the evidence does not isolate the runtime from the model. Each comparison uses a different model family and parameter size on the two systems, so you cannot infer that RIPL will be faster for your model or traffic pattern without testing.
| Reported comparison | RIPL model | Comparison model on vLLM | Liquid AI’s reported result | How to interpret it |
|---|---|---|---|---|
| Workload 1 | LFM2.5-1.2B | Qwen3.5-0.8B | 14.4–28.4× lower p95 latency on RIPL | Vendor-reported 2026 result; different model families and sizes mean it is not a runtime-only comparison. |
| Workload 2 | LFM2.5-2.6B | Qwen3.5-2B | 5.6–7.7× lower p95 latency on RIPL | Vendor-reported 2026 result; request shape, hardware and quality conditions must be reproduced before applying it to another deployment. |
Both ranges are from Liquid AI’s October 22, 2026 announcement, linked above. They indicate the performance Liquid AI is targeting for real-time LFM serving, not a guarantee for another GPU, prompt length, output length, concurrency level, quantization, or model quality target.
Why p95 matters more than an average
p95 latency is the time at or below which 95 percent of requests finish. A service can have an attractive average while a meaningful tail of users waits much longer. For interactive search, assistants, checkout flows, or other synchronous paths, measure first-token latency, time to last token, and p95 or p99 under the concurrency you actually expect. Also record errors, queue time, and output quality; a lower latency number is not useful if the runtime drops requests or changes the model’s behavior.
What is the difference between RIPL, vLLM, TensorRT-LLM, and llama.cpp?
The four projects optimize for different deployment decisions. The table below summarizes the strongest reasons established by their official materials to put each one into your evaluation.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Runtime | Best-supported reason to evaluate it | Questions to answer before selecting it |
|---|---|---|
| Liquid AI RIPL | Liquid Foundation Models in latency-sensitive enterprise request paths on named NVIDIA GPUs. | Is the workload an LFM? Is your exact GPU and model supported? Does measured tail latency meet the service budget? |
| vLLM | Production serving with PagedAttention, continuous batching, parallelism, quantization, distributed inference, model integration, and an OpenAI-compatible API. | Does the selected vLLM version support your model, quantization and accelerator? How do throughput and tail latency change at expected concurrency? |
| TensorRT-LLM | NVIDIA-focused model optimization plus online and offline serving workflows. | Does the model and GPU have the required feature support? Can your team operate NVIDIA’s build, conversion and deployment workflow? |
| llama.cpp | Lightweight local or embedded inference over a wide range of operating systems and hardware, commonly using GGUF model artifacts. | Do you have a compatible artifact? What speed, memory use and output quality result from the selected quantization and backend? |
For feature and hardware details, consult the official vLLM documentation, NVIDIA’s AI Inference Backends comparison, and TensorRT-LLM documentation. llama.cpp’s local model path and GGUF context are also discussed in Liquid AI’s LFM2.5 on-device announcement.
Which runtime is best for a latency-sensitive enterprise API?
Start with RIPL if you are serving an LFM on a supported NVIDIA GPU and latency is the primary acceptance criterion. Keep vLLM or TensorRT-LLM in the bakeoff when you need broader model choice, a more general serving stack, or a different operational workflow.
Choose RIPL when the model and hardware match its design
RIPL is the most focused option in this comparison. Its stated purpose is to put LFMs directly in a live request path, and the launch benchmarks emphasize p95 latency. That focus can be valuable when every millisecond affects conversion, search interaction, or another synchronous user action.
Do not treat an NVIDIA GPU name alone as proof of compatibility. Confirm the exact GPU generation, model checkpoint, context length, precision or quantization mode, batching behavior, and required drivers with Liquid AI. The announcement names H100, H200, B200, and RTX PRO 6000, but it does not publish a complete support matrix or installation specification.
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vLLM is a general-purpose serving choice when you need continuous batching, PagedAttention, parallelism, quantization, distributed inference, and an OpenAI-compatible API. Its documentation covers model integrations and multiple hardware options, making it a practical baseline for teams serving several model families or changing models over time.
That breadth creates more variables to validate. Support depends on the vLLM version, model architecture, accelerator backend, and selected features. Benchmark queueing and tail latency at realistic concurrency instead of assuming that a throughput-oriented configuration will also win a single-user interactive test.
Choose TensorRT-LLM when NVIDIA optimization is the operating model
TensorRT-LLM is suited to teams deliberately targeting NVIDIA hardware and willing to use NVIDIA’s optimization and serving workflow. Its documentation covers online and offline serving, model features, and hardware-specific capabilities.
The trade-off is engineering specificity. Model conversion, engine building, precision choices, and feature support must line up with the target GPU and TensorRT-LLM release. NVIDIA’s materials establish an optimization path; they do not establish that TensorRT-LLM wins every model, batch size, or latency objective. Include build time, upgrade effort, and rollback procedures in your evaluation.
Choose llama.cpp for local, embedded, or offline execution
llama.cpp is the natural candidate when inference must run on a workstation, edge device, desktop application, or other environment where a compact runtime and broad hardware reach matter more than a centralized service control plane. NVIDIA’s backend comparison presents it as a lightweight option across varied systems, while Liquid AI documents GGUF among local model paths for its on-device models.
Your model must be available in a format and architecture that the chosen llama.cpp build supports. Quantization can reduce memory requirements and improve speed, but it can also alter output quality. Measure the quality target you care about—such as structured-output validity or task accuracy—alongside latency and memory use.
How should I compare the four runtimes fairly?
A useful bakeoff changes one factor at a time and reports both service performance and model quality. Follow this sequence before choosing a production runtime.
- Freeze the model. Use the same checkpoint or the closest technically equivalent artifact in every runtime. Record model family, parameter count, context window, tokenizer, revision and any conversion steps. If a runtime cannot load the same model, document the substitution instead of presenting the result as a runtime-only comparison.
- Fix the hardware and software environment. Use the same GPU model, host CPU, memory, driver family, operating-system image and power settings where possible. Record the runtime version, CUDA or other backend version, precision, quantization, kernel choices and serving configuration.
- Define request shapes. Create representative short, medium and long prompts, and set explicit output limits. Include your real system prompt, tool or structured-output requirements, and stop conditions. Prompt and output lengths strongly affect both first-token and total latency.
- Test the traffic levels that matter. Measure a single request for responsiveness, then step through the expected concurrent load and a reasonable stress level. For batched servers, record batch size, queue time, active sequences and throughput as concurrency rises.
- Capture percentile metrics. Report time to first token, time per generated token, total completion time, p50, p95 and p99, plus requests per second and output tokens per second. Separate queueing, prefill and decode time when the runtime exposes them.
- Verify quality and reliability. Run a fixed evaluation set and check exact-match or task-specific scores, JSON validity, refusal behavior, truncation, out-of-memory errors, timeouts and restarts. A speed result that fails your quality or availability target is not a production win.
- Repeat after warm-up and during changes. Discard or label cold-start measurements. Repeat enough times to expose variance, then rerun after changing quantization, drivers, model revisions or runtime versions. Keep the test harness and raw results so a future upgrade can be compared with the same conditions.
For RIPL specifically, reproduce the model and hardware conditions behind Liquid AI’s published p95 claims only if you have access to the same artifacts and settings. Otherwise, label your result as a separate workload measurement, not a confirmation of the announcement.
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RIPL checks
- Confirm that the checkpoint is a Liquid Foundation Model and that the current RIPL release supports its architecture and context length.
- Confirm support for the planned NVIDIA H100, H200, B200, or RTX PRO 6000 deployment—or another GPU only if Liquid AI explicitly documents it.
- Obtain current installation, driver, container, networking, observability, scaling and licensing requirements from Liquid AI.
- Ask how RIPL handles batching, streaming responses, autoscaling, failures, upgrades and multi-tenant isolation.
vLLM checks
- Match the model architecture and quantization method to the exact vLLM release and accelerator backend.
- Verify the API features you need, including streaming, structured output, tool calling or parallel execution, rather than assuming every integration behaves identically.
- Measure memory headroom for the model, KV cache, batch scheduler and maximum context length at peak concurrency.
TensorRT-LLM checks
- Verify that the model architecture, precision and required features appear in NVIDIA’s current support matrix.
- Plan for engine generation and conversion time, and test whether an engine built for one GPU or software stack can be reused on another.
- Include rebuild and rollback procedures in release management; an optimized engine is an artifact with its own lifecycle.
llama.cpp checks
- Confirm that the model artifact is supported by your build, commonly through GGUF for local deployments.
- Choose and record quantization deliberately, then test quality degradation on your own prompts.
- Measure RAM or VRAM use, thread count, offload settings, context size and thermal behavior on the actual device.
How do latency, throughput, privacy, and operations change the decision?
No single metric determines the winner. Map the runtime to the service property that is hardest to compromise.
| Priority | What to measure | Likely starting point |
|---|---|---|
| Strict interactive latency | First-token and p95/p99 completion latency at expected concurrency. | RIPL for supported LFMs; compare against vLLM and TensorRT-LLM on the same workload. |
| High aggregate throughput | Output tokens per second, requests per second, queue time and GPU utilization under sustained load. | vLLM or TensorRT-LLM, depending on model, GPU and optimization workflow. |
| Many models or changing backends | Model coverage, API compatibility, upgrade effort and deployment portability. | vLLM, with version-specific support verification. |
| NVIDIA-specific optimization | Engine build performance, precision support, utilization and operational repeatability. | TensorRT-LLM, after confirming model and GPU features. |
| Local or offline operation | On-device latency, memory footprint, battery or thermal limits, and privacy behavior. | llama.cpp when a compatible model artifact is available. |
For local applications, include whether prompts and outputs remain on the device, how updates are delivered, and what happens without network access. For servers, include batching, concurrency limits, observability, authentication, autoscaling, graceful shutdown and incident recovery. These are deployment requirements to test, not assumptions that all four runtimes provide identical controls.
What are the most common selection mistakes?
Treating vendor benchmarks as universal rankings
The RIPL figures are compelling within Liquid AI’s stated comparisons, but the models differ in family and size. Publishing them as proof that one runtime always beats vLLM would overstate the evidence. Reproduce your own workload and keep the qualification attached to every reported number.
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Optimizing average latency while ignoring the tail
A low average can hide queue buildup or long stalls at peak concurrency. Set an explicit p95 or p99 objective and test it with realistic traffic, not only an idle single-request loop.
Comparing different quantization or quality targets
A heavily quantized model may be faster and smaller than a higher-precision model, but that is a model-and-configuration trade-off, not a pure runtime advantage. Keep precision and quality evaluation visible in the result.
Assuming model format portability
A checkpoint that loads in one server may require conversion or may not be supported in another. Check architecture, tokenizer, context handling, quantization and required operators before scheduling a performance test.
Ignoring operational cost
Include image build time, engine compilation, deployment automation, monitoring, upgrades, rollback, support channels and failure recovery. A few milliseconds saved in a lab may not offset a workflow your team cannot maintain.
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RIPL’s launch announcement does not provide a complete public prerequisite and pricing reference. Confirm those details, as well as current feature support for every runtime, against the exact release and hardware you plan to deploy.
What is the practical decision process?
Use a staged funnel rather than trying to benchmark every possible combination at once.
- Eliminate incompatible options. Remove runtimes that cannot load your model, run on your accelerator, meet your operating-system constraints, or satisfy your privacy and network requirements.
- Build a two- or three-runtime shortlist. For an LFM on a named NVIDIA GPU, include RIPL and at least one general alternative. For a multi-model service, include vLLM. For an NVIDIA-only optimization program, include TensorRT-LLM. For local deployment, include llama.cpp.
- Run the same harness. Keep prompts, output limits, concurrency steps, warm-up policy and quality checks identical. Store raw latency samples, not only averages.
- Apply production gates. Require the chosen runtime to meet latency percentiles, quality, memory, error rate, observability and recovery targets simultaneously.
- Re-test after material changes. A new model revision, quantization, GPU, driver or runtime release can change the result. Treat the benchmark as a repeatable regression test.
This process prevents a headline benchmark from deciding a deployment whose real bottleneck is model compatibility, queueing, quality or operations.
Frequently asked questions
Can I use llama.cpp with Liquid Foundation Models?
It depends on the specific LFM checkpoint and the model format supported by your llama.cpp build. Liquid AI’s on-device material discusses GGUF as a local model path, but you should verify the exact architecture, conversion requirements and quantization for the model revision you intend to run.
Does vLLM provide an OpenAI-compatible endpoint?
Yes, the official vLLM documentation lists an OpenAI-compatible API among its serving capabilities. Compatibility still needs endpoint-level testing for the features your client uses, such as streaming, structured output, tool calls and error handling.
Is TensorRT-LLM only for online serving?
No. NVIDIA documents both online and offline serving workflows. Which path is appropriate depends on whether you need an interactive service, a batch job, or both, and on the model and GPU features supported by the selected release.
What should I record when reporting an inference benchmark?
Record the model revision, tokenizer, prompt and output lengths, hardware, software versions, precision or quantization, concurrency, warm-up policy, percentile latencies, throughput, errors and quality results. Without those conditions, another team cannot tell whether a difference came from the runtime or from the workload setup.
Frequently Asked Questions
Can I use llama.cpp with Liquid Foundation Models?
It depends on the specific LFM checkpoint and the model format supported by your llama.cpp build. Liquid AI’s on-device material discusses GGUF as a local model path, but you should verify the exact architecture, conversion requirements and quantization for the model revision you intend to run.
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Does vLLM provide an OpenAI-compatible endpoint?
Yes, the official vLLM documentation lists an OpenAI-compatible API among its serving capabilities. Compatibility still needs endpoint-level testing for the features your client uses, such as streaming, structured output, tool calls and error handling.
Is TensorRT-LLM only for online serving?
No. NVIDIA documents both online and offline serving workflows. Which path is appropriate depends on whether you need an interactive service, a batch job, or both, and on the model and GPU features supported by the selected release.
What should I record when reporting an inference benchmark?
Record the model revision, tokenizer, prompt and output lengths, hardware, software versions, precision or quantization, concurrency, warm-up policy, percentile latencies, throughput, errors and quality results. Without those conditions, another team cannot tell whether a difference came from the runtime or from the workload setup.
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