[
    {
        "role": "user",
        "content": "what%20are%20some%20of%20the%20top%20models%20available%20for%20use%20on%20copilot%20plus%20ai%20pcs%3F%20can%20linux%20be%20used%20on%20these%20pcs%3F%20can%20it%20access%20the%20npu%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a breakdown of what\u2019s known about top models, Linux support, and accessing the NPU on Microsoft Copilot+ PCs (as of May 2026):\n\n---\n\n## Which \u201ctop\u201d models are available on Copilot+ PCs\n\n\u201cModels\u201d in this context refers to AI or language models that are optimized to run directly on the NPU or device hardware. Key ones include:\n\n- **Phi Silica** \u2014 A small language model (SLM) developed by Microsoft in the Phi-3 family; it has ~3.3 billion parameters and is optimized for NPU use on Copilot+ PCs. It enables features like summarization and rewriting of text in Word/Outlook locally. ([blogs.windows.com](https://blogs.windows.com/windowsexperience/2024/12/06/phi-silica-small-but-mighty-on-device-slm/?utm_source=openai))  \n- **Distilled DeepSeek R1 models** \u2014 These are models optimized via the ONNX format to work locally on Snapdragon-powered Copilot+ PCs. They offer fairly high throughput (about 25\u201340 tokens/sec) and low token latency for short prompts. ([blogs.windows.com](https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/?utm_source=openai))  \n\nBesides those, the Windows AI environment includes **built-in AI components** such as Image Creation, Image Processing, Image Transform, and also tools integrated for context-driven tasks like Live Caption translation etc., all using built-in models tuned to utilize the NPU. ([support.microsoft.com](https://support.microsoft.com/en-us/topic/windows-copilot-ai-components-a9ef14e9-32a7-497f-b780-7b6fb63af793?utm_source=openai))\n\n---\n\n## Can Linux be used on these machines? What's the state of support\n\nYes\u2014Linux *can be installed and run* on many Copilot+ PC hardware platforms (especially ARM-based ones like Snapdragon X Elite), but with limitations. Support is partial and still maturing. Key points:\n\n- Qualcomm is upstreaming support for the **Snapdragon X Elite** SoC to the mainline Linux kernel. Kernel versions 6.8, 6.9 already have many essential pieces (early boot, UEFI support, basic device trees) merged. More work in progress includes support for display, GPU, power management, camera, etc. ([qualcomm.com](https://www.qualcomm.com/developer/blog/2024/05/upstreaming-linux-kernel-support-for-the-snapdragon-x-elite?utm_source=openai))  \n- Some early attempts to build fully Linux-based machines (e.g. by TUXEDO Computers) with Snapdragon X Elite were **cancelled**, citing that the chip turned out to be \u201cless suitable than expected.\u201d Among the issues were missing or incomplete drivers, firmware, power management, and subpar battery life under Linux. ([windowscentral.com](https://www.windowscentral.com/hardware/qualcomm/tuxedo-scraps-its-linux-based-snapdragon-x-elite-laptop-says-the-soc-proved-to-be-less-suitable-for-linux-than-expected?utm_source=openai))  \n\nSo while Linux installs are happening, **hardware driver coverage is not yet complete**, especially for the NPU, GPU, display, etc. Many features are in progress but not fully functional on many distributions.\n\n---\n\n## Can the NPU be accessed and used under Linux?\n\nThis is the trickiest question, and the answer is: **not fully yet**. Here\u2019s what I found:\n\n- Under **Windows**, model inference on NPUs is well supported. Windows ML, ONNX Runtime, DirectML, etc., allow apps and built-in features to use the NPU. Models like Phi Silica and DeepSeek R1 are explicitly optimized for this. ([learn.microsoft.com](https://learn.microsoft.com/windows/ai/npu-devices/?utm_source=openai))  \n- Under **Linux**, there is **no mature, fully stable stack** for accessing the Hexagon NPU (or other NPUs on Copilot+ PCs) in general availability. Several users and Linux-oriented laptop makers report missing driver support for many aspects of the hardware NPU subsystem, including firmware, user-space drivers, and API/SDKs. ([reddit.com](https://www.reddit.com/r/linux/comments/1dmhmpl?utm_source=openai))  \n\nQualcomm\u2019s upstream kernel patches include hardware detection, basic OS boot, but not yet full NPU acceleration. Also, parts of the firmware and binary blobs needed for NPU functionality often remain proprietary and tied to Windows. ([reddit.com](https://www.reddit.com/r/snapdragon/comments/1s0ibbv/why_qualcomm_wont_support_linux_on_snapdragon/?utm_source=openai))\n\n---\n\n## Summary \u2014 What you *get* vs what still required\n\nHere\u2019s a summary table to lay out the status:\n\n| Capability | Under Windows on Copilot+ PC | Under Linux |\n|------------|-------------------------------|-------------|\n| Run pre-installed Microsoft models (Phi Silica, DeepSeek, etc.) using NPU | \u2705 Fully supported; optimized for NPU; built-in OS components ([support.microsoft.com](https://support.microsoft.com/en-us/topic/windows-copilot-ai-components-a9ef14e9-32a7-497f-b780-7b6fb63af793?utm_source=openai)) | \u2753 Likely **not fully supported** yet; mostly CPU fallback or mixed support; NPU acceleration for these models is not broadly available |\n| ONNX / direct model inference (custom models) using NPU | \u2705 Yes, via Windows ML, ONNX Runtime, DirectML; is encouraged for developers. ([learn.microsoft.com](https://learn.microsoft.com/windows/ai/npu-devices/?utm_source=openai)) | \u2753 Very early; driver/firmware gaps; not reliable yet |\n| Full hardware functionality (GPU, display, power management) under Linux | \u2014 (Windows is default) | \u26a0\ufe0f Partial; often missing or buggy in snapshot kernels; some components work, others still being upstreamed ([qualcomm.com](https://www.qualcomm.com/developer/blog/2024/05/upstreaming-linux-kernel-support-for-the-snapdragon-x-elite?utm_source=openai)) |\n\n---\n\n## What to watch for / when things might improve\n\n- **Newer hardware** like **Snapdragon X2 Elite** (seen in some reports coming in early-to-mid 2026) improves performance and may have better driver support. ([windowscentral.com](https://www.windowscentral.com/hardware/qualcomm/snapdragon-x2-elite-vs-x-elite?utm_source=openai))  \n- **Kernel versions 6.10-6.11 and newer** are expected to add more driver support for video, display, GPU, etc. But getting full NPU access requires firmware and driver components from Qualcomm/OEMs. ([windowsreport.com](https://windowsreport.com/snapdragon-x-elite-on-linux/?utm_source=openai))  \n- Projects like **Hexagon-MLIR** (an open signal for better graphics / AI compilation support for the Hexagon NPU), and community efforts to build user-space SDKs/drivers are steps in that direction. ([arxiv.org](https://arxiv.org/abs/2602.19762?utm_source=openai))  \n\nIf having NPU access under Linux is essential, you\u2019ll need to check:  \n1. Whether your specific Copilot+ PC model has any Linux-firmware support for the NPU.  \n2. Whether the model of the NPU (e.g. Qualcomm Hexagon) has any open or documented drivers.  \n3. Whether any community builds or OEMs for your model have already achieved partial or full NPU acceleration.\n\n---\n\nIf you want, I can check your specific model (if you tell me what Copilot+ PC you have or plan to get) and see whether NPU under Linux works or is likely to work. Would that be useful?"
    },
    {
        "role": "user",
        "content": "how%20would%20i%20use%20Distilled%20DeepSeek%20R1%20on%20windows%3F%20is%20there%20a%20cli%3F%20does%20it%20need%20to%20be%20in%20some%20sort%20of%20chassis%20or%20is%20it%20self-contained%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s what I found on how to use **Distilled DeepSeek R1** on Windows\u2014including what interfaces are available, how \u201cself-contained\u201d it is, and what you need to run it. If you want, I can also walk you through your specific setup.\n\n---\n\n## Interfaces & How to Run It\n\nYes, there *are* command-line and GUI options; how you use DeepSeek R1 depends on what you\u2019re aiming for.\n\n- **Ollama**  \n  This is one of the most popular ways to run DeepSeek R1 locally on Windows. You can pull specific Distilled variants (1.5B, 7B, 14B, etc.) and run them from the command line. For example:  \n  ```\n  ollama run deepseek-r1:1.5b\n  ollama run deepseek-r1:7b\n  ```  \n  ([beebom.com](https://beebom.com/how-run-deepseek-r1-locally/?utm_source=openai))\n\n- **LM Studio**  \n  A GUI-based tool for running local models. It supports loading DeepSeek R1 distilled variants; good for those who prefer not to use the terminal. ([beebom.com](https://beebom.com/how-run-deepseek-r1-locally/?utm_source=openai))\n\n- **AI Toolkit in VS Code**  \n  Microsoft has announced that on Copilot+ PCs, you can use the **AI Toolkit** extension in VS Code. It offers a \u201cPlayground\u201d where you can download and test DeepSeek R1 Distilled models (starting with the 1.5B) and run them locally. ([blogs.windows.com](https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/?utm_source=openai))\n\n---\n\n## Self-Contained vs Dependencies\n\nDeepSeek R1 Distilled is fairly self-contained as a model (you can run it locally without needing constant cloud access), but there *are* dependencies:\n\n- You need the model files (weights), which are distributed in formats like **GGUF** or ONNX, depending on your execution environment. ([sitepoint.com](https://www.sitepoint.com/deepseek-r1-local-deployment-guide-2026/?utm_source=openai))  \n- To run it realistically (for non-trivial size like 7B+), you\u2019ll want proper hardware: sufficient RAM / VRAM (GPU), good CPU, etc. ([sitepoint.com](https://www.sitepoint.com/deepseek-r1-local-deployment-guide-2026/?utm_source=openai))  \n- Tools like Ollama or LM Studio provide infrastructure (runtime, quantization support, etc.). The model is not just the weights\u2014you get runtime/loader tools. AI Toolkit also includes its own \"Playground\" + runtime optimized for Copilot+ PCs. ([blogs.windows.com](https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/?utm_source=openai))  \n\nSo yes, it *is* possible to run it fully locally/self-contained if you have the setup, but there are external pieces involved (runtime, tooling).\n\n---\n\n## What It Needs: Hardware & Quantization\n\nDeepSeek R1 Distilled models come in a range of sizes. Here\u2019s what you\u2019ll want in terms of hardware and performance thresholds:\n\n- **Small versions** (1.5B, 7B): modest VRAM (a few GB), works well on average consumer GPUs or even integrated GPUs. ([willitrunai.com](https://willitrunai.com/blog/deepseek-r1-gpu-requirements?utm_source=openai))  \n- **Medium versions** (14B, 32B): significantly more VRAM to fully offload to GPU; on the order of ~8-20+ GB depending on quantization. Otherwise, you might need partial offloading or use CPU fallback. ([sitepoint.com](https://www.sitepoint.com/deepseek-r1-local-deployment-guide-2026/?utm_source=openai))  \n- **Quantization** is essential for efficiency. Formats like Q4_K_M and Q8_0 are frequently used: lower-bit formats reduce memory usage at cost to precision. The Distilled DeepSeek R1 versions offered for Copilot+ PCs are optimized (ONNX QDQ format, etc.) for NPU + CPU mixed calculations. ([blogs.windows.com](https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/?utm_source=openai))  \n\n---\n\n## Does it Need a Special \u201cChassis\u201d or Device?\n\nNo\u2014DeepSeek R1 Distilled doesn\u2019t require a special physical chassis; you can run it on any PC that meets the hardware & software requirements. Things to consider:\n\n- If you want to **utilize the NPU** (as on Copilot+ PCs), the model must be compiled/optimized for it (e.g. ONNX QDQ format with parts mapped to NPU) and run in an environment that supports NPU usage (e.g. Windows Copilot Runtime). This is what Microsoft is doing with their version of Distilled DeepSeek R1 on Snapdragon-powered Copilot+ PCs. ([blogs.windows.com](https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/?utm_source=openai))  \n- If you\u2019re using a \u201cregular\u201d Windows PC without a compatible NPU, you\u2019ll be using GPU and/or CPU. In that case, you want to match the model size and quantization to your GPU\u2019s VRAM. ([willitrunai.com](https://willitrunai.com/blog/deepseek-r1-gpu-requirements?utm_source=openai))  \n\n---\n\n## Recap: How You\u2019d Use It on Windows\n\nPutting it all together, here\u2019s how to get going:\n\n1. Choose your variant (1.5B, 7B, 14B, etc.) depending on your hardware.  \n2. Install a tool: Ollama (CLI), LM Studio (GUI), or perhaps VS Code + AI Toolkit (if on Copilot+ PC).  \n3. Download the weights (open weights available for distilled variants).  \n4. Run the model via the tool. CLI usage might look like `ollama run deepseek-r1:7b`. GUI tools let you \"load\" model and interact.  \n5. If you have an NPU-capable device with support (such as certain Copilot+ PCs), use the model optimized for that framework (ONNX/Distilled version, Windows Copilot Runtime) to gain speed/efficiency benefits.  \n\n---\n\nIf you tell me the exact specs of your Windows PC (CPU, GPU, or whether it\u2019s a Copilot+ model, etc.), I can recommend which variant will run well for *you*, and walk you through installing and running it step by step."
    },
    {
        "role": "user",
        "content": "good.%20so%20if%20i%20want%20to%20use%20llama%2C%20and%20rely%20on%20npu%20in%20a%20copilot%20plus%20p%2C%20what%20sort%20of%20hardware%20should%20i%20look%20for%3F%20ram%3F%20npu%3F"
    },
    {
        "role": "assistant",
        "content": "If you're planning to run LLaMA (or similar large language models) on a Copilot+ PC and want to lean on the NPU for inference, here are the key hardware / spec requirements to aim for \u2014 and what you can realistically expect given current constraints.\n\n---\n\n## What Microsoft requires for Copilot+ PCs\n\nFirst, to *qualify* as a Copilot+ PC, Microsoft currently mandates:\n\n- An NPU capable of **\u2265 40 TOPS** (trillion operations per second). ([microsoft.com](https://www.microsoft.com/en-us/windows/learning-center/copilot-plus-pcs-windows-pcs-differences?utm_source=openai))  \n- At least **16 GB RAM**. ([microsoft.com](https://www.microsoft.com/en-us/windows/learning-center/copilot-plus-pcs-windows-pcs-differences?utm_source=openai))  \n- A **256 GB SSD** or larger. ([microsoft.com](https://www.microsoft.com/en-us/windows/learning-center/copilot-plus-pcs-windows-pcs-differences?utm_source=openai))  \n- Up-to-date OS: Windows 11 version 24H2 or newer. ([microsoft.com](https://www.microsoft.com/en-us/windows/learning-center/copilot-plus-pcs-windows-pcs-differences?utm_source=openai))  \n\nThese are minimums; you\u2019ll need more for smooth local LLaMA-style workloads. Microsoft also recommends using ONNX Runtime (for inference) and tools like Olive for model optimization. ([learn.microsoft.com](https://learn.microsoft.com/en-au/windows/ai/npu-devices/?utm_source=openai))\n\n---\n\n## What hardware you should aim for if you want to run LLaMA well\n\nHere are more aggressive specs that will help you get better performance if you\u2019re planning to run LLaMA models locally, especially if you want moderate batch sizes, more context, or responsive performance.\n\n| Component | Target / Ideal Spec | Why It Matters for LLaMA + NPU |\n|-----------|----------------------|-----------------------------------|\n| **NPU** | \u2265 40 TOPS (good) \u2022 \u2265 60\u201380 TOPS (better) | Larger NPUs handle more throughput and bigger model parts before falling back to CPU/GPU. The 40 TOPS baseline helps unlock Copilot+ features; higher TOPS gives room for larger LLaMA model parts. ([aipc.computer](https://aipc.computer/blog/copilot-pc?utm_source=openai)) |\n| **System RAM (main memory)** | 16 GB minimum \u2022 32 GB+ preferred \u2022 48\u201364 GB if you want to run large contexts or larger models (7B\u201313B or bigger) | RAM is needed to hold model components not handled by the NPU, intermediate activations, OS, etc. Smaller RAM = more swapping or slowdowns. ([tomshardware.com](https://www.tomshardware.com/software/windows/microsoft-copilot-pcs-all-we-know?trk=public_post-text&utm_source=openai)) |\n| **Storage** | Fast SSD \u2014 NVMe, ideally large enough to hold model files (several GBs to tens of GBs depending on model size) \u2014 512GB or more is safer. | SSD speed matters for loading weights, caching. Larger models take more disk space. Microsoft\u2019s Copilot+ spec is 256 GB minimum. ([microsoft.com](https://www.microsoft.com/en-us/windows/learning-center/copilot-plus-pcs-windows-pcs-differences?utm_source=openai)) |\n| **CPU / GPU** | Strong CPU to handle parts of model offloaded; GPU beneficial if NPU can\u2019t handle everything. GPU with 8+ GB VRAM helps for fallback. | When NPU capacity is exceeded, CPU/GPU can take over, so better components reduce performance cliffs. |\n| **Temperature / Cooling** | Good thermal design (efficient cooling) | NPUs and CPUs under load generate heat; thermal throttling can kill performance, especially for longer prompts / contexts. |\n\n---\n\n## How LLaMA-style models currently map to NPU\n\nWhen using LLaMA locally (via tools like `llama.cpp`, Ollama, etc.), the model has to be:\n\n- **Quantized** (usually to 8-bit, 4-bit, or even lower) so it fits into NPU-friendly formats (INT8, etc.). Running full FP32 or FP16 is too heavy.  \n- Possibly split so parts run on NPU (for the math-heavy inference steps) and parts on CPU or GPU. If your NPU can\u2019t handle all layers (depending on its supported formats), you\u2019ll see fallback to CPU/GPU, which is slower.\n\nONNX Runtime (with Windows ML / Windows AI SDK) is Microsoft\u2019s recommended path for accessing NPU for inference. Model conversion / optimization tools (e.g., Olive) are used to prepare LLaMA models (or variants) to run well on the NPU. ([learn.microsoft.com](https://learn.microsoft.com/en-au/windows/ai/npu-devices/?utm_source=openai))\n\n---\n\n## Practical recommendations (for a \u201cgood\u201d LLaMA experience)\n\nTo balance cost, performance, and usability, here\u2019s what I'd recommend:\n\n- Choose a Copilot+ PC with **NPU \u2265 60 TOPS** if possible (better headroom for future models).\n- Get **32 GB RAM** if you want comfortable performance. If you\u2019re only doing short prompts with small LLaMA variants (4B, 7B), 16 GB might suffice\u2014but performance will be tighter.\n- **SSD** should be fast (NVMe) and plenty of space; many models already are tens of GBs.\n- A discrete GPU helps if NPU is overloaded or model is huge.\n- Make sure the software chain is in place: ONNX Runtime with NPU backends, tool for quantizing model to NPU-friendly format, etc.\n\n---\n\nIf you tell me *which LLaMA variant(s)* you want to run (e.g. LLaMA-2 or 3, 7B vs 13B vs 70B), I can estimate more concrete hardware targets so you can buy accordingly."
    },
    {
        "role": "user",
        "content": "let%27s%20say%20i%27m%20aiming%20for%20a%2013b%20model.%20first%20off%2C%20what%20does%2013b%20do%20that%207b%20can%27t%20do.%20second%2C%20which%20thinkpad%20models%20have%20the%20upper%20end%20npu%20over%2060%20tops%20and%20plenty%20of%20ram%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a more detailed comparison of what a 13B language model does differently (vs 7B), and then a rundown of ThinkPad / ThinkPad-like PCs with NPUs > 60 TOPS + ample RAM that would be strong candidates for running 13B models with good performance.\n\n---\n\n## What a 13B model offers compared to a 7B\n\nA \u201c13B\u201d model refers to around 13 billion parameters; a \u201c7B\u201d model has ~7 billion. The difference in parameter count translates into several qualitative and quantitative improvements:\n\n| Area | 7B Model Strengths | What 13B Adds / Improves |\n|---|---------------------|----------------------------|\n| Factual accuracy & hallucination control | Handles basic questions reasonably well; less prone to totally bizarre outputs for short prompts. | Better at precision, fewer errors; more reliable with less prompting or smaller context. ([ultraaiguide.com](https://ultraaiguide.com/llama-2-series-vs-7b-which-model-wins/?utm_source=openai)) |\n| Contextual understanding & memory | Can capture context over moderate prompt lengths; performance starts dropping in long chains. | Holds longer contexts better; more coherent when reasoning across multiple steps or lengthy text. ([bacloud.com](https://www.bacloud.com/en/blog/163/guide-to-gpu-requirements-for-running-ai-models.html?utm_source=openai)) |\n| Complex reasoning & specialized domains (coding, math, legal, scientific) | Decent reasoning in general use; may struggle with advanced or deeply technical cases. | Better reasoning, handling of niche domains; improved output in code generation, complex logic tasks. ([dasroot.net](https://dasroot.net/posts/2026/03/best-7b-13b-models-production-quality-performance-comparison/?utm_source=openai)) |\n| Natural language quality | Good fluency; simpler phrasing; okay creativity. | More nuanced grammar, style, and creativity; better suited for writing tasks, speech-like responses, literary or nuanced writing. ([ultraaiguide.com](https://ultraaiguide.com/llama-2-series-vs-7b-which-model-wins/?utm_source=openai)) |\n| Cost & hardware demands | Much lighter: lower RAM/VRAM demand; runs on less capable hardware; faster inference per token. | Heavier: needs more VRAM/quantization support; more memory; more CPU/NPU power; may have higher latency unless optimized or accelerated. ([bacloud.com](https://www.bacloud.com/en/blog/163/guide-to-gpu-requirements-for-running-ai-models.html?utm_source=openai)) |\n\nSo, if you care about more reliable performance in reasoning, better output quality, longer context handling, or more industry-standard tasks (code, math, technical writing), a 13B model is a noticeable upgrade. If you mainly do lightweight chat, simple summarization, or very short prompts, 7B might suffice.\n\n---\n\n## Hardware needs for running a 13B model well (on a Copilot+ PC)\n\nTo run 13B on-device with a good experience (reasonable latency, acceptable throughput), here\u2019s what I recommend:\n\n- **Memory (RAM / VRAM)**: At least 24 GB of VRAM (or its equivalent when quantized or offloaded), or around **32 GB system RAM** if you're relying on the NPU + CPU combination with some fallback or memory sharing. More RAM helps significantly.  \n- **NPU performance**: A chip capable of > 60 TOPS ideally. That gives you headroom so that your model\u2019s parts (especially the critical layers) can be mapped to the NPU without flooding CPU/GPU fallback.  \n- **Fast SSD storage** to load weights quickly and cache activations.  \n- **Good CPU** otherwise, since parts of model (especially attention, feed-forward layers) may run on CPU when NPU is maxed.  \n- **Optimized model pipeline**: quantization (4-bit or 8-bit), high memory bandwidth between CPU/NPU. Using ONNX Runtime + Windows AI stack helps.\n\n---\n\n## ThinkPad / Lenovo models & others with NPUs \u2267 60 TOPS\n\nI searched for ThinkPads (or similar Lenovo machines) that meet or exceed 60 TOPS NPU and have enough RAM to support 13B. The results show that **most current ThinkPad Copilot+ PCs** are still in the 40\u201350 TOPS range. There *are*, however, new chips coming that push NPUs up to ~80 TOPS.\n\nHere are relevant models / chipsets:\n\n| Device / Chip | NPU TOPS | RAM & other notes | Suitability for 13B |\n|---|-----------|---------------------|----------------------|\n| **Snapdragon X2 Elite & X2 Elite Extreme** chips | ~ 80 TOPS for high-end variants ([tomsguide.com](https://www.tomsguide.com/computing/cpus/qualcomm-announces-snapdragon-x2-elite-extreme-cpus-and-its-as-powerful-as-you-think?utm_source=openai)) | Often 48 GB LPDDR5X in high-end SKUs; tight in some ultrabooks but capable ([itpro.com](https://www.itpro.com/hardware/laptops/the-asus-zenbook-a16-is-an-impressive-debut-for-qualcomms-snapdragon-x2?utm_source=openai)) | Strong candidate: 80 TOPS gives good NPU headroom; with ~48 GB RAM, you can run 13B with quantization or hybrid offloading. |\n| **ThinkPad T14s Gen 6 (Snapdragon X Elite)** | ~ 45 TOPS NPU ([windowscentral.com](https://www.windowscentral.com/hardware/laptops/lenovo-thinkpad-t14s-gen-6-snapdragon-x-announced?utm_source=openai)) | Up to 64 GB RAM in some SKUs; though not always 60+ TOPS ([codebrewtools.com](https://codebrewtools.com/blogs/best-ai-laptop-2026-npu-benchmarks?utm_source=openai)) | Borderline: 45 TOPS likely means parts of the 13B model will fall back to CPU; performance might suffer unless model heavily quantized. |\n| **ThinkPad P16v Gen 3** | unspecified dedicated NPU; uses Intel Core Ultra with dedicated AI features ([storagereview.com](https://www.storagereview.com/review/lenovo-thinkpad-p16v-gen-3-review-rtx-pro-2000-benchmarked-in-a-4-6-lb-workstation?utm_source=openai)) | Likely more RAM; workstation class ([storagereview.com](https://www.storagereview.com/review/lenovo-thinkpad-p16v-gen-3-review-rtx-pro-2000-benchmarked-in-a-4-6-lb-workstation?utm_source=openai)) | Might be possible depending on NPU strength in particular SKU, but without confirmed TOPS \u2265 60, more risk. |\n\n---\n\n## Recommendation: Which ThinkPads to target\n\nGiven what\u2019s currently available, here\u2019s what you should aim for if you're serious about running a 13B model on a Copilot+ ThinkPad:\n\n- Wait for / target a ThinkPad with **Snapdragon X2 Elite (or X2 Elite Extreme)** chip with its ~80 TOPS NPU. These are just starting to ship. That gives you good headroom.  \n- Ensure the unit has **32 GB RAM** minimum, 48 GB or 64 GB if possible. Memory is often soldered on ARM-based boards, so pick a configuration with enough from the get-go.  \n- Make sure the SSD is fast and large enough (1 TB is safer), because model files (especially quantized variants) still take several GBs.  \n- Look into cooling/design: even with strong NPUs, if the device throttles heavily, performance will suffer. Workstation or premium thin-and-light designs with solid thermal design help.  \n\n---\n\nIf you want, I can look up *specific upcoming ThinkPad SKUs* with Snapdragon X2 Elite, 80 TOPS, 48+ GB RAM that are launch-ready or already shipping (in US/your region). Would you prefer me to pull those together so you can compare price/performance?"
    }
]