Runanywhere Sdks
Hand-written GPU/NPU kernels for sub-10ms on-device AI inference, with open-source SDKs for every platform.
RunAnywhere is a top pick for teams shipping on-device AI on Apple or Qualcomm silicon. The hand-written kernels deliver speeds generic runtimes can't touch—MetalRT hits 658 tok/s decode on M4 Max, and QHexRT is the first engine to run LLM, VLM, STT, TTS, and embeddings 100% on Hexagon NPUs. The open-source SDKs with a single C++ core make cross-platform deployment practical, even enabling a solo dev to port an Android app to iOS in six weeks. But contact-only pricing and research-heavy docs will frustrate casual users—pick it when latency is the bottleneck, otherwise MLX or llama.cpp might be easier.
Verified 2d ago · liveness 55/100 · cite: rightaichoice.com/tools/runanywhere-sdks
- Teams building low-latency on-device voice or vision apps on Apple Silicon or Qualcomm NPUs
- Cross-platform mobile developers needing one SDK for iOS and Android with native performance
- Privacy-first products that must run fully offline with no cloud dependency
- Performance engineers who need hand-optimized kernels and published, reproducible benchmarks
- Beginners looking for no-code AI tools or managed cloud APIs
- Teams requiring NVIDIA GPU or CUDA support (only Apple and Qualcomm targets)
- Projects that prefer simple, self-serve pricing and instant sign-up — RunAnywhere is contact-only
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip RunAnywhere if you need NVIDIA GPU or CUDA support, want instant self-serve pricing, or prefer a beginner-friendly managed cloud API—it's contact-only and targets Apple/Qualcomm silicon with a kernel-level, research-heavy approach.
Contact-based pricing may require a sales call and custom contract, with no published price list to compare against.
RunAnywhere uses contact-only pricing, so there's no public price to compare. It's aimed at teams with budget for performance and custom contracts. For budget-conscious developers, MLX and llama.cpp are free open-source alternatives, though they won't match the hand-optimized kernel speeds.
In short
Runanywhere Sdks — Hand-written GPU/NPU kernels for sub-10ms on-device AI inference, with open-source SDKs for every platform. Best for Teams building low-latency on-device voice or vision apps on Apple Silicon or Qualcomm NPUs, Cross-platform mobile developers needing one SDK for iOS and Android with native performance, Privacy-first products that must run fully offline with no cloud dependency. Contact Sales pricing.
What's new in Runanywhere Sdks
Checked 2 days agoAcross the latest 5 updates: 3 feature updates, 1 launch and 1 changelog entry.
We Put a 27B Model in Your Pocket: PrismML Bonsai 1-bit on iPhone, Android, and Mac
RunAnywhere apps now run PrismML Bonsai 1-bit 27B LLM on-device across iOS, Android, and macOS, including the first 1-bit model on any NPU.
Android to iOS in Six Weeks, with the RunAnywhere SDK
A solo developer ported his $2K MRR local-AI app to iOS in six weeks using the RunAnywhere SDK.
QHexRT Is Live: Full-Stack NPU Inference for Qualcomm Hexagon
QHexRT inference engine launches for Qualcomm Hexagon NPUs, running LLM, VLM, STT, TTS, and embeddings 100% on-device with LFM 2.5 230M achieving 12,540 tok/s prefill.
MetalRT Now Does Speech-to-Speech. 1.52x Faster Than mlx-audio.
MetalRT adds native speech-to-speech support with 1.68s end-to-end latency, 123 tok/s throughput, and 1.52x faster than mlx-audio on M4 Max.
MetalRT Now Runs Vision Language Models. Fastest on Apple Silicon.
MetalRT adds VLM support with 279 tok/s vision decode, 92ms time-to-output, and 1.22x faster than mlx-vlm on M4 Max.
What people actually say about Runanywhere Sdks — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
4 mentions across 1 source (Hacker News) · researched Jul 3, 2026.
- +Hand-optimized Metal GPU kernels for Apple Silicon performance.
- +Achieves 45 tokens/s on iPhones for on-device LLMs.
- +Open-source SDKs for Swift, Kotlin, React Native, Flutter, Web.
- +Sub-10ms inference latency on local devices.
- +Automatic cloud routing based on cost, latency, or privacy.
- −Sent unsolicited GitHub-scraped emails, harming developer trust.
- −Very sparse community feedback and third-party benchmarks.
- −Pricing is opaque (only 'contact us').
- −Not yet proven at scale or in production environments.
- −Limited platform support beyond Apple Silicon and Qualcomm.
- • Cloud routing may incur third-party API costs (e.g., OpenRouter)
- • Enterprise tier likely requires annual contract
Viability Score
How well maintained and how widely used is Runanywhere Sdks? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Hand-written Metal kernels for Apple M-series GPUs (MetalRT)
- 100% NPU inference for Qualcomm Hexagon NPUs (QHexRT)
- LLM inference with 658 tok/s decode and 6.6ms TTFT on M4 Max
- VLM support with 279 tok/s vision decode and 1.22x speedup over mlx-vlm
- Speech-to-speech with 1.68s end-to-end latency, 1.52x faster than mlx-audio
- Speech-to-text and text-to-speech on-device inference
- Embeddings support
- PrismML Bonsai 1-bit 27B model on-device (first 1-bit model on NPU)
- Open-source SDKs: Swift, Kotlin, React Native, Flutter, TypeScript, C++
- One C++ core shared across all six SDKs (runanywhere-core)
- Cross-platform support: iOS, Android, macOS, Windows, Linux, web, embedded
- Hosted console for fleet operations and OTA model updates
- Automatic cloud routing when needed
- Published reproducible benchmarks with methodology disclosure
- Web demo to try in browser
About Runanywhere Sdks
RunAnywhere is a Y Combinator-backed inference lab that hand-writes GPU and NPU kernels to extract maximum speed from consumer silicon, primarily Apple M-series GPUs and Qualcomm Hexagon NPUs. Instead of relying on generic runtimes, the team builds engines from scratch: MetalRT for Apple GPUs and QHexRT for Qualcomm Hexagon NPUs, both backed by published benchmarks. The result is on-device inference with sub-10ms latency—658 tok/s decode and 6.6ms time-to-first-token on an M4 Max, and 12,540 tok/s prefill for LFM 2.5 230M on Hexagon v81. For teams shipping AI in mobile or edge apps, this delivers responsiveness cloud APIs can't match, with privacy as a default since everything runs locally. Verified capabilities cover LLMs, vision-language models, speech-to-text, text-to-speech, and speech-to-speech—the latter hitting 1.52x faster than mlx-audio on M4 Max. Recent milestones: QHexRT launched in June 2026, running LLMs, VLMs, STT, TTS, and embeddings 100% on Qualcomm Hexagon NPUs. In March 2026, MetalRT added speech-to-speech and vision language model support, with VLM decode reaching 279 tok/s. In July 2026, PrismML Bonsai, a 27B 1-bit LLM, went live on-device across iOS, Android, and macOS, including the first true 1-bit model ever on an NPU. The open-source layer above the kernels includes a C++ core (runanywhere-core) with thin SDK bindings for Swift, Kotlin, React Native, Flutter, TypeScript, and C++. One API and one set of models across iOS, Android, macOS, Windows, Linux, web, and embedded. Hosted console adds fleet management and OTA model updates. RunAnywhere targets teams that need peak performance and full control over their inference stack, especially those shipping cross-platform.
Behind the Verdict
RunAnywhere stands out because it doesn't just wrap existing runtimes—it writes the kernels from scratch for each target silicon. This means you get numbers you can't get elsewhere: 658 tok/s decode and 6.6ms TTFT on an M4 Max, 12,540 tok/s prefill on Hexagon NPUs, and a 27B 1-bit model running on a phone. For product teams, the honest advantage is responsiveness and privacy: everything runs locally, so there's no cloud round-trip. The cross-platform story is unusually strong: one C++ core with six SDKs means you write once and ship to iOS, Android, macOS, Windows, Linux, web, and embedded. The open-source core means you can inspect and even modify the runtime. Weaknesses: contact-only pricing means you can't just sign up; the docs redirect to an external site, so onboarding may take extra steps. It's research-heavy—you'll need to understand kernel-level concepts to get the most out of it. Community support is thinner than MLX or llama.cpp, and third-party tutorials are scarce. If you're using NVIDIA GPUs, this won't help. Where it fits: teams shipping latency-sensitive voice or vision features on Apple or Qualcomm devices, or cross-platform apps that want native performance without maintaining separate codebases. Where it doesn't: beginners wanting quick cloud APIs, or teams on NVIDIA hardware.
Researching Runanywhere Sdks? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Runanywhere Sdks actually fits — and what changes day-one when you adopt it.
You need on-device LLM inference in both iOS and Android apps.
Outcome: Integrate the Swift or Kotlin SDK, use the same C++ core, and ship with sub-10ms latency on both platforms.
You need to run a 27B VLM on consumer hardware.
Outcome: Deploy PrismML Bonsai 1-bit on-device via RunAnywhere, achieving 279 tok/s vision decode on M4 Max.
You have a local-AI Android app and want to expand to iOS.
Outcome: Port your app to iOS in six weeks using the RunAnywhere SDK, reusing the same models and API.
Use Cases
- Run LLM inference entirely on-device on Apple Silicon with sub-7ms TTFT.
- Deploy a vision agent that processes live video locally and routes only relevant frames to a cloud VLM.
- Build a fully offline AI assistant on Android that listens, reasons, and speaks back.
- Create a cross-platform mobile app with on-device AI using a single SDK for iOS, Android, and Web.
- Achieve sub-200ms voice RAG pipeline entirely on-device without any cloud dependencies.
- Run a 27B parameter LLM on a phone using the PrismML Bonsai 1-bit model.
Models Under the Hood
as of 2026-08-17
Limitations
- RunAnywhere targets Apple Silicon (M-series GPU) and Qualcomm Hexagon NPU only.
- No NVIDIA GPU or CUDA support.
- Contact-only pricing with no self-serve tier, which may delay onboarding.
- Documentation redirects to an external site, adding setup friction.
- Advanced kernel-level engineering knowledge is likely needed for full utilization.
- Community support is thinner than established open-source projects like MLX or llama.cpp.
as of 2026-08-20
Verification history
We have re-verified Runanywhere Sdks 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Runanywhere Sdks's pricing actually pencils out — and where peers do it cheaper.
RunAnywhere uses contact-only pricing, so there's no public price to compare. It's aimed at teams with budget for performance and custom contracts. For budget-conscious developers, MLX and llama.cpp are free open-source alternatives, though they won't match the hand-optimized kernel speeds.
Setup time & first value
How long it actually takes to get something useful out of Runanywhere Sdks — broken out by persona, not the marketing-page minute.
For a developer familiar with the SDKs, first integration can be done in a day or two, with full cross-platform deployment in under a week. Performance tuning for specific silicon may take additional time for kernel-level optimization.
Switching to or from Runanywhere Sdks
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From llama.cpp: Replace the runtime with RunAnywhere to gain hand-optimized kernel speeds on Apple/Qualcomm hardware.
- ↗To llama.cpp: If you need NVIDIA support or broader community, you can migrate your model weights and reimplement inference with llama.cpp.
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Runanywhere Sdks vs Temporal Ai
Temporal and RunAnywhere solve fundamentally different problems. Temporal is the no-compromise platform for building fault-tolerant, long-running AI agent workflows with full state persistence, making it ideal for teams that need reliability at scale. RunAnywhere excels at deploying AI models on-device with sub-10ms latency, perfect for mobile and edge apps prioritizing privacy and speed. Choose Temporal if you need orchestration and reliability; choose RunAnywhere if you need local inference with cross-platform SDKs.
Runanywhere Sdks vs Spider Cloud
These tools serve completely different needs. Choose RunAnywhere if you need to run AI models on-device with low latency and privacy; choose Spider Cloud if you need to fetch and structure live web data for AI agents or RAG. They complement each other but are not direct competitors.
Runanywhere Sdks vs Voyage Ai
If you need high-accuracy retrieval embeddings for enterprise RAG (e.g., finance, legal), Voyage AI is the specialist—its domain-specific models and low-dimensional vectors cut storage costs. But if you're building mobile or edge apps that demand sub-10ms on-device inference with full privacy, RunAnywhere's MetalRT and QHexRT engines are unmatched. The two tools solve different problems: one optimizes cloud retrieval, the other local execution. Choose based on your deployment target.
Popular in Local & On-Device AI
Unsloth
Run and train LLMs locally with Unsloth — a free, open-source desktop app for Mac, Windows, and Linux.
Cortex.cpp
Run 123+ open-source models locally or connect online APIs in one free, open-source desktop app
Frequently Asked Questions
Used Runanywhere Sdks? Help shape our editorial sentiment research.