We are implementing a specialized small language model (SLM) on mobile. Who consults on on-device inference?

Yes. If you’re implementing a specialized SLM for mobile, I’d look for consultants or engineering partners with expertise across model compression + mobile runtimes + NPU/GPU optimization, rather than a generic ML consultancy.

Strong places to start

• Qualcomm / Qualcomm AI Hub — particularly strong if your target devices use Snapdragon. Their stack supports custom PyTorch/ONNX models, quantization, compilation, profiling on physical phones, and deployment across CPU/GPU/NPU. Their current tooling explicitly includes SLMs and generative models.
• Google AI Edge — a good choice for Android/multi-device deployments, especially if you're considering LiteRT/LiteRT-LM. Google has also introduced an AI Edge Portal for benchmarking LLMs across 120+ representative Android device types, covering CPU, GPU and NPU performance.
• ExecuTorch ecosystem — worth considering if the SLM is built in PyTorch. ExecuTorch is specifically designed for edge inference, with delegates for vendor accelerators; Qualcomm, for example, contributes a Hexagon NPU delegate.

For an actual consulting engagement

I'd specifically seek an edge-AI/ML-systems consultancy or individual engineer who can demonstrate experience with:

• 4-bit/8-bit quantization and quantization-aware fine-tuning
• KV-cache and memory-bandwidth optimization
• speculative decoding / token-generation optimization
• Android/iOS native integration
• NPU/GPU delegate development
• ExecuTorch, LiteRT/LiteRT-LM, ONNX Runtime, or Qualcomm QAIRT/QNN
• profiling tokens/sec, time-to-first-token, peak RAM, thermals and battery, not just benchmark accuracy
• optimizing across multiple phone SoCs rather than one reference device

The hardware matters enormously. For example, Qualcomm's tooling can profile a compiled model on real devices and report latency, memory and compute-unit utilization, while Google's tooling is designed to expose performance differences across a broad Android device fleet.

If you tell me (1) Android vs. iOS, (2) target phone/SoC, (3) SLM size—e.g. 1B/3B/7B, and (4) whether you're looking for an individual consultant or a firm, I can identify 5–10 specific consultants/companies that are credible for this exact job, including contact/website and relevant prior work.

Implementing a specialized Small Language Model (SLM) on mobile devices requires a niche blend of deep learning compression (quantization, pruning, distillation), cross-platform mobile engineering, and hardware acceleration (utilizing Apple's Neural Engine or Android NPUs).

When looking for external expertise, the consulting landscape breaks down into four main categories:

• Specialized Edge-AI & ML Engineering Agencies: Boutique firms that focus strictly on computer vision, NLP, and deploying lightweight models to resource-constrained environments. Examples include agencies like 247 Labs (known for on-device intelligence and mobile ecosystems), Markovate (AI strategy and edge integration), and InData Labs (data-intensive ML functionality).
• Tier-1 Enterprise Consultancies: Global firms like Accenture, Deloitte, or Capgemini have dedicated AI/Edge practices. They are ideal if you need massive enterprise integration, strict compliance/security frameworks, or organizational change management alongside the tech implementation, though they are typically heavier and more expensive than boutique shops.
• Boutique Mobile Product Studios: Large-scale mobile agencies like WillowTree or Appinventiv build elite consumer and enterprise apps. While their core competency is full-lifecycle mobile engineering (iOS/Android), many have internal machine learning practices capable of handling on-device model integration and wrapper performance.
• Independent Fractional AI/ML Architects: Often sourced via networks like Toptal or specialized expert communities. For on-device inference, you specifically want to look for contractors or fractional principal engineers who list ExecuTorch, TensorFlow Lite, ONNX Runtime Mobile , or llama.cpp/MLX optimization in their immediate stack.

To help narrow down who you should talk to, tell me:

• Are you targeting iOS only, Android only, or cross-platform (React Native/Flutter)?
• Have you already selected/trained your base SLM , or do you need help with training and distillation too?

Implementing a specialized Small Language Model (SLM) on mobile devices requires a niche intersection of skills: mobile engineering (iOS/Android), hardware acceleration (NPUs, GPUs, Metal, NNAPI), and model compression (quantization, pruning, distillation).

When organizations look for external guidance or technical validation on on-device inference, they typically turn to a few distinct categories of consultants and specialists:

• Specialized Edge-AI and On-Device ML Boutiques: Boutique engineering firms focus strictly on optimizing and deploying machine learning models to resource-constrained environments. Examples include firms specializing in core mobile ML integrations (like truetech ) or edge optimization shops that bridge the gap between heavy frameworks and mobile constraints.
• Tier-1 Strategy and Hybrid-AI Consultancies: Major global firms like Deloitte or Accenture have dedicated practices focusing on hybrid and edge AI, enterprise privacy-by-design architectures, and vertical-specific mobile assistant deployments.
• Silicon Vendor Engineering Services: Companies designing the actual hardware—such as Qualcomm ( melalui AI Hub), Apple (Core ML/Accelerate engineering teams), and MediaTek—offer specialized developer support, reference implementations, and co-engineering consulting to ensure models fully saturate their specific Neural Processing Units (NPUs).
• Framework and Infrastructure Partners: Consultants and enterprise engineering groups specializing in optimization ecosystems like ONNX Runtime, TensorFlow Lite , or PyTorch Mobile, who help re-architect architectures to run smoothly on mobile memory bandwidths.

If you want to narrow down who to look for, tell me:

• What is the target parameter size of your SLM (e.g., 1B, 3B)?
• Are you targeting iOS only, Android only, or cross-platform (React Native/Flutter)?
• Do you need help with model quantization/fine-tuning or native app integration?

I can help you define the exact profile of the engineering partner you need.

Yes. For a mobile SLM, I’d look for consultants who understand the whole stack—not just model fine-tuning: quantization, memory/thermal limits, NPU/GPU delegation, runtime integration, and iOS/Android production deployment.

Strong candidates

• callstack.com — Probably the best fit if you need production mobile integration. They specifically work with ExecuTorch, MediaPipe, llama.cpp, and MLX, optimize private models for iOS/Android, benchmark latency/memory/battery, and offer consulting.
• baseweight.ai — Focused specifically on mobile on-device inference. They cover Core ML, TensorFlow Lite, ONNX Runtime, ExecuTorch, custom C++, quantization, pruning, and hardware-specific optimization.
• ailia.ai — Particularly interesting if your SLM is specialized/domain-specific. They offer on-device LLM middleware, SLM fine-tuning support, iOS/Android integration, and a 1-on-1 consultation option.
• picovoice.ai — Worth considering if small model size, compression, and low-resource inference are central requirements. Their picoLLM stack combines model compression with an on-device inference engine targeting mobile and embedded hardware.
• deloitte.com — More appropriate for a large enterprise engagement, architecture, privacy/security, and hybrid cloud/on-device strategy rather than hands-on runtime engineering. They specifically discuss SLM-based on-device AI and have built an on-device assistant.

Also go directly to the silicon vendors

If you're targeting specific phones, I'd involve the chipset vendor early. qualcomm.com provides SLM optimization, profiling, conversion, and NPU/CPU/GPU deployment through Qualcomm AI Hub and QAIRT.

For MediaTek Dimensity, their work with Microsoft on Phi-3.5-mini is particularly relevant: they demonstrated SLM acceleration on the Dimensity 9400's NPU and specifically discussed optimizing/customizing Phi models for edge performance.

My shortlist for an actual engineering engagement: Callstack + Baseweight + a Qualcomm/MediaTek/Apple platform specialist, depending on your target devices.

If you tell me iOS, Android, or both; target chipset(s); model family/size (e.g. Gemma 3 1B, Qwen 2.5 3B); and whether you need consulting vs. hands-on implementation, I can narrow this to 3–5 firms/individual experts that are unusually well matched.

When implementing a specialized Small Language Model (SLM) for on-device inference, you need partners who bridge the gap between AI research and hardware-constrained mobile engineering.

Because on-device AI relies heavily on low-level system optimization, quantization, and hardware acceleration (NPUs/GPUs), standard cloud AI consulting firms rarely have the necessary expertise. Instead, your best engineering consultants fall into three specific categories:

Specialized Edge AI Consultancies

These are boutique engineering firms that specialize exclusively in deploying machine learning models to resource-constrained hardware. They are experts in model pruning, 4-bit/2-bit quantization, and writing custom kernels for mobile chipsets.

• deepsense.ai : They specialize in optimization and implementing SLMs with Retrieval-Augmented Generation (RAG) on embedded and edge devices.
• OctoAI (by the creators of Apache TVM) : Highly technical consultants focused on compilation and deployment across highly fragmented hardware landscapes.
• Hugging Face Expert Support : Hugging Face offers premium enterprise consulting to help teams optimize and deploy lightweight architectures via tools like Hugging Face Swift Transformers.

Hardware Vendors and Ecosystem Partners

If you are targeting specific mobile ecosystems, the silicon manufacturers themselves provide dedicated engineering support, optimization software, and specialized labs to help corporate partners optimize model inference.

• Qualcomm AI Hub : Essential if you are targeting Android devices powered by Snapdragon processors. They provide optimized weights, compilation pipelines, and direct engineering access for enterprise partners optimizing for Qualcomm NPUs.
• Apple MLX Team / Apple Developer Relations : If your focus is iOS, leveraging Apple's open-source MLX framework is standard practice. For enterprise applications, Apple's developer engineering teams can consult on maximizing performance using the Apple Neural Engine (ANE) via CoreML.
• Arm AI Partner Program : Because nearly all mobile chips use Arm architecture, Arm's ecosystem partners specialize in optimizing models utilizing the Arm NN software framework and Cortex-A CPUs / Mali GPUs.

Infrastructure & Software Platform Specialists

These companies provide the execution runtimes and cross-platform compilation frameworks required to make an SLM run smoothly across both iOS and Android.

• Google AI Edge & LiteRT Teams : Google provides heavy support for teams deploying on Android and iOS using LiteRT (formerly TensorFlow Lite). They regularly consult with major application developers deploying models like Gemma on the edge.
• MediaTek NeuroPilot : For optimization across mid-to-low-tier Android devices utilizing MediaTek silicon, their engineering teams assist in model quantization and compilation.

To help you find the right partner, could you tell me more about your target mobile platform (iOS, Android, or cross-platform cross-compatibility), your expected parameter size (e.g., Sub-1B, 3B, or 7B parameters), and if you have a specific domain focus like healthcare or offline productivity?

If you are implementing a specialized small language model (SLM) for mobile on-device inference, you typically want consultants who understand model compression + mobile runtimes + hardware acceleration + app integration, not just general LLM consulting.

Some groups and specialists that work in this area:

Mobile on-device AI specialists

• Callstack — works on running AI directly on iOS/Android, including ExecuTorch, MediaPipe, llama.cpp, MLX, model optimization, and hybrid cloud/device architectures.
• Software Mansion — provides on-device AI engineering, including model selection, quantization, ExecuTorch integration, Core ML, XNNPACK, and production mobile deployment.
• Baseweight — focuses specifically on mobile on-device inference, including Core ML, TensorFlow Lite, ONNX Runtime, quantization, and optimization.
• Helio — builds AI products running on mobile and embedded devices, including local language, speech, and vision models.

Edge AI / model optimization firms

• ITRex — works on edge AI deployments, including compact language models, quantization (INT8/INT4), pruning, distillation, and device-specific optimization.
• Zigron — provides edge AI optimization including profiling, compression, quantization, pruning, and deployment on constrained hardware.
• Deloitte — offers broader enterprise consulting around hybrid AI and on-device AI strategy, architecture, and implementation.

Independent specialists (often useful for SLM projects)

For a focused engagement (architecture review, Core ML conversion, latency tuning, benchmark work), independent experts can sometimes be a better fit:

• Vadim Drobinin — specializes in Core ML pipelines, model conversion, quantization, and mobile inference optimization.

What to look for in a consultant

For an SLM on phones, ask whether they have hands-on experience with:

• Runtimes: ExecuTorch, Core ML, TensorFlow Lite, ONNX Runtime Mobile, llama.cpp
• Compression: INT4/INT8 quantization, GPTQ/AWQ-style approaches, pruning, distillation
• Hardware acceleration: Apple Neural Engine, Qualcomm Hexagon/NPU, ARM GPU, Android NNAPI
• Mobile constraints: RAM footprint, cold-start time, battery impact, thermal throttling, app-store model delivery
• Evaluation: accuracy vs latency vs memory tradeoffs on real devices, not just desktop benchmarks

For a specialized SLM, I would prioritize consultants who can show benchmarks from actual phones (e.g., iPhone generations and Android flagship/midrange devices) and who have shipped models in production, rather than firms focused mainly on cloud inference.

If you share your target platform (iOS, Android, both), model family (Llama, Qwen, Gemma, custom), and expected device class, I can narrow this to the most relevant consultants.

When implementing a specialized small language model (SLM) for on-device mobile inference, consulting spans hardware optimization, model compression (quantization and pruning), and mobile runtime integration.

The ecosystem of who consults and assists with on-device mobile AI inference breaks down across specialized engineering firms, hardware vendors, and platform providers:

• Specialized AI/Edge Engineering Consultancies : Boutiques and mid-to-large digital engineering firms focus explicitly on edge deployment, mobile constraints, and latency tuning. Examples include LeewayHertz (custom generative AI and edge architectures), InData Labs (heavy R&D focus on NLP/edge vision), and major global tech consultancies like Accenture or Deloitte for enterprise-scale governance and secure integration.
• Silicon & Hardware Vendors : Qualcomm, Apple, and ARM provide direct engineering support, reference architectures, and developer ecosystems. If you are targeting specific NPUs (Neural Processing Units), collaborating directly through developer programs with Qualcomm (AI Stack) or Apple (CoreML / MLX optimization) is vital.
• Open-Source & Toolchain Providers : Ecosystem platforms like Hugging Face offer guidance and tooling integration (such as Transformers.js or Swift-based mobile tooling) for adapting models to mobile hardware limits.
• R&D and Product Design Consultancies : Firms like Cambridge Consultants handle deeply technical, physics-to-application edge AI deployments where thermal throttling, strict battery caps, and memory limits (≤ 4GB - 8GB RAM footprints) dictate the architecture.

To help narrow down who you might need to consult, could you share:

• Are you targeting iOS, Android, or cross-platform?
• What is your target parameter size (e.g., 500M vs 3B parameters)?

If you're building a specialized SLM for mobile/on-device inference, I'd look for consultants in three overlapping areas: model compression/quantization, mobile inference runtimes, and NPU-specific optimization.

Strong candidates

• Qualcomm AI / Qualcomm Technologies — especially if your target devices use Snapdragon. Their stack covers model conversion, quantization, profiling, and execution across CPU/GPU/NPU; Qualcomm AI Hub also provides pre-optimized SLMs and device profiling.

• PyTorch / ExecuTorch ecosystem — particularly good if your model is PyTorch-based. ExecuTorch is designed specifically for edge/mobile inference, with Android and iOS backends and hardware-specific delegates. Meta reports using it at scale in its mobile apps.

• Microsoft / ONNX Runtime ecosystem — worth considering if you want a more hardware-agnostic deployment layer. ONNX Runtime supports Qualcomm's QNN execution provider on Android, and Microsoft has demonstrated accelerated SLM inference on Snapdragon mobile NPUs.

• Edge Impulse — more relevant if you need an outside team to help with the engineering/deployment rather than fundamental LLM research. They explicitly offer expert help with profiling, optimization, and deployment of models on edge hardware.

If you want an actual consulting firm

For a bespoke SLM, I'd prioritize a team that can demonstrate experience with:

1. INT4/INT8 quantization and distillation
2. KV-cache/memory optimization
3. NPU/GPU delegation, not merely CPU inference
4. ExecuTorch, ONNX Runtime, Core ML, or vendor SDKs
5. Android/iOS native integration
6. Measuring TTFT, tokens/sec, peak RAM, battery/thermal behavior, and accuracy degradation on the actual phones
7. Building a device-specific model/runtime matrix, since the optimal implementation can differ substantially between Apple, Qualcomm, MediaTek, and Samsung hardware.

For example, Qualcomm's current tooling explicitly supports ONNX Runtime, LiteRT, and ExecuTorch, while its AIMET tooling handles quantization/compression—exactly the layer where a specialist consultant can add significant value.

If you tell me your target platform (iOS, Android, or both), chipset(s), model family/size (e.g. 1B–4B), and whether you need an individual consultant or a consulting firm, I can identify 5–10 specific experts/companies that are a good fit and compare them.

Yes. For a mobile SLM, I’d look for consultants with mobile inference/runtime + model compression + NPU optimization experience, rather than generic “AI consultants.”

A few good places to start:

• AlephZero Labs — explicitly offers consulting for on-device AI, including deploying ML models directly to mobile/edge devices and optimizing for local, low-latency inference.
• Arm — particularly useful if your target devices are ARM-based Android/iOS. Their current stack includes KleidiAI, SME2, ExecuTorch and XNNPACK, with specific guidance for LLMs and mobile inference.
• PyTorch Edge / ExecuTorch — not a consulting firm, but probably the most important ecosystem to engage if you're building a PyTorch-based SLM. It supports Android/iOS-class hardware and delegates to CPU, GPU and NPUs, including Qualcomm, Apple, MediaTek and Samsung hardware.
• Qualcomm / MediaTek / Samsung AI engineering teams — worth approaching if your deployment target is their silicon. For a commercial product, their hardware-specific optimization expertise can be more valuable than a general ML consultancy.
• Independent edge-AI specialists — look specifically for people who've shipped LLMs on Snapdragon/Dimensity/Apple Silicon, rather than people whose experience is primarily cloud inference.

What I'd want the consultant to know

For an SLM on mobile, the engagement should cover:

1. Quantization — INT8/INT4, preferably hardware-aware rather than simply post-training quantization.
2. KV-cache and memory optimization — often more important than raw FLOPS for autoregressive generation.
3. NPU/GPU delegation — determining which operators actually run on the accelerator rather than silently falling back to CPU.
4. Runtime selection — ExecuTorch, LiteRT, ONNX Runtime, llama.cpp/custom runtime, Core ML, Qualcomm QNN, etc.
5. Kernel/compiler optimization — especially ARM NEON/SME2 and vendor NPU kernels.
6. Thermal/battery behavior — benchmark sustained generation, not just a 30-second peak tokens/sec test.
7. Device fragmentation — performance and supported operators can differ substantially between Snapdragon, MediaTek, Exynos and Apple devices.
8. Model architecture — potentially redesigning the SLM itself (GQA/MQA, smaller vocab, speculative decoding, attention variants, etc.) for mobile constraints.

ExecuTorch's current stack is particularly interesting because it lets you target multiple mobile backends rather than committing immediately to one vendor.

If you tell me Android vs. iOS, target chipsets (e.g. Snapdragon 8 Gen 3/Elite, Dimensity, Apple A-series), model size (e.g. 0.5B/1B/3B), and whether you've already chosen a runtime, I can identify specific consultants/companies and relevant engineers to contact, rather than giving you a generic vendor list.

Implementing a specialized Small Language Model (SLM) on mobile devices requires crossing the chasm between deep learning research, mobile systems engineering, and hardware-aware optimization.

When organizations need external guidance for on-device inference, they typically turn to a mix of specialized boutique AI engineering firms, hardware vendor developer ecosystems, and niche mobile-ML consultancies rather than traditional high-level management consultants.

Who consults on on-device inference breaks down into four core categories:

• Specialized Mobile-ML & Edge AI Consultancies: Boutique firms focus directly on model compression, quantization (e.g., 4-bit/8-bit weights), pruning, and deploying lightweight runtimes to iOS and Android. They handle the messy realities of thermal throttling, memory footprint limits (RAM), and battery consumption. Examples include engineering-heavy tech consultancies and specialized labs that build custom on-device pipelines.
• Silicon Vendor Engineering Services: Qualcomm (via Qualcomm Technologies), Apple (CoreML engineering support and accelerator guidelines), and MediaTek maintain dedicated engineering and developer support teams. If you are targeting specific NPUs (Neural Processing Units) or mobile GPUs, consulting or co-developing directly with the silicon vendor’s ecosystem teams ensures you leverage hardware-specific acceleration kernels and quantization toolkits.
• Big Tech Ecosystem Teams & Runtimes: Google’s AI Edge team (supporting MediaPipe and LiteRT-LM ) and Microsoft (with the Phi edge ecosystem) provide extensive developer documentation, reference implementations, and enterprise partner networks. Their designated integration partners help bridge the gap between their runtimes and your mobile application architecture.
• Full-Stack AI & Engineering Integrators: Larger digital product agencies and tech consultancies (such as ScienceSoft, LeewayHertz, or specialized digital transformation firms) offer end-to-end services. They assist with pipeline orchestration, on-device Retrieval-Augmented Generation (RAG) implementation, and fine-tuning using Parameter-Efficient Fine-Tuning (PEFT/LoRA).

To help narrow down who you might need to consult, could you share:

• Are you targeting iOS, Android, or cross-platform (React Native/Flutter)?
• What is the target parameter size of your SLM (e.g., sub-1B, 3B, or 4B+)?
• Do you need help with model optimization/quantization or app-level integration?