[
    {
        "role": "user",
        "content": "what%20are%20dimensions%20in%20inches%20of%20dell%20precision%203680%20workstation%3F"
    },
    {
        "role": "assistant",
        "content": "The Dell Precision 3680 Tower workstation has the following dimensions in inches:  \n\n- Height: **14.68 in** ([delltechnologies.com](https://www.delltechnologies.com/asset/en-us/products/workstations/technical-support/precision-3680-spec-sheet.pdf?utm_source=openai))  \n- Width: **6.81 in** ([delltechnologies.com](https://www.delltechnologies.com/asset/en-us/products/workstations/technical-support/precision-3680-spec-sheet.pdf?utm_source=openai))  \n- Depth: **16.54 in** ([delltechnologies.com](https://www.delltechnologies.com/asset/en-us/products/workstations/technical-support/precision-3680-spec-sheet.pdf?utm_source=openai))  \n\nIf you need the weight or dimensions in another unit (mm or cm), I can provide those too."
    },
    {
        "role": "user",
        "content": "what%20gpu%20are%20supported%20in%20it%3F"
    },
    {
        "role": "assistant",
        "content": "Here are the discrete GPUs officially supported in the Dell Precision 3680 Tower, along with key technical requirements like power supply (PSU), card dimensions, and connectors:  \n\n---\n\n### Supported Discrete GPUs\n\nAccording to the official specification, these discrete (non-integrated) graphics cards are supported:\n\n#### NVIDIA\n- RTX 6000 Ada Generation \u2013 48 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- RTX 5000 Ada Generation \u2013 32 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- RTX 4500 Ada Generation \u2013 24 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- RTX 4000 Ada Generation \u2013 20 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- RTX 2000 Ada Generation \u2013 16 GB GDDR6 ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- RTX A1000 \u2013 8 GB GDDR6 ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- RTX A400 \u2013 4 GB GDDR6 ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- T1000 (8 GB & 4 GB) \u2013 GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- T400 \u2013 4 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- GeForce RTX 4090 / RTX 4090D \u2013 24 GB GDDR6X ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- GeForce RTX 4080 Super \u2013 16 GB GDDR6X ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- GeForce RTX 4070 \u2013 12 GB GDDR6 ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n- GeForce RTX 4060 \u2013 8 GB GDDR6 ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&lang=en-us&utm_source=openai))  \n\n#### AMD\n- Radeon Pro W7900 \u2013 48 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- Radeon Pro W7600 \u2013 8 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- Radeon Pro W7500 \u2013 8 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- Radeon Pro W6400 \u2013 4 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- Radeon Pro W6300 \u2013 2 GB GDDR6 ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n\n---\n\n### Key Compatibility Requirements & Constraints\n\nHere\u2019s what you need to pay attention to when installing any of the above GPUs in a Precision 3680 Tower:\n\n| Requirement | Details |\n|---|---|\n| Power Supply (PSU) | The system can be configured with 300 W, 500 W, or 1000 W Platinum power supply units. Larger and more power-hungry GPUs require the 1000 W PSU. ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/power-ratings?guid=guid-3f65a560-cf4e-45a2-8b9c-0354dc9cd703&lang=en-us&utm_source=openai)) |\n| Card Length | The maximum card length supported is about **12.28 inches (with extender)** for the largest cards (e.g. RTX 4090). Without extender, slightly less (~12.01 inches). ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpu-and-psu-matrix?guid=guid-3f6cc2a8-d6d7-4cf8-9bd9-6168e24a4950&utm_source=openai)) |\n| Connectors & Slots | Requires a full-height PCIe x16 slot (Gen5) for discrete GPU. Power connectors are typically 16-pin for high-end cards (RTX 4090, RTX 6000, etc.), or 8-pin for mid-range ones. ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpu-and-psu-matrix?guid=guid-3f6cc2a8-d6d7-4cf8-9bd9-6168e24a4950&utm_source=openai)) |\n| Width / Slot Space | Some GPUs are dual-wide (2 slots) and some require up to triple slot spacing. ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpu-and-psu-matrix?guid=guid-3f6cc2a8-d6d7-4cf8-9bd9-6168e24a4950&utm_source=openai)) |\n| Cooling & Physical Fit | Ensuring good airflow and physical fit within the case is necessary\u2014especially with thicker or longer GPUs. The extender cable may be required for the longest cards. ([dell.com](https://www.dell.com/support/manuals/de-de/precision-t3680-workstation/precision_3680_tower_om/gpu-und-psu-matrix?guid=guid-3f6cc2a8-d6d7-4cf8-9bd9-6168e24a4950&utm_source=openai)) |\n\n---\n\nIf you tell me which PSU version you have (300 W, 500 W, or 1000 W), or which GPU model you're considering, I can help confirm whether your setup will support it!"
    },
    {
        "role": "user",
        "content": "1000%20watts%2C%20rtx%204090"
    },
    {
        "role": "assistant",
        "content": "Yes \u2014 with a 1000W PSU, a Dell Precision 3680 Tower *can* support an NVIDIA GeForce RTX 4090, but there are several compatibility details you need to make sure are met. Here\u2019s a breakdown:\n\n---\n\n### \u2705 What Dell\u2019s Official Specs Say\n\nFrom Dell\u2019s documentation:\n\n- The Precision 3680 has a PCIe Gen5 \u00d716 full-height slot that supports **discrete graphics cards up to 450W** when properly powered. ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- The **GPU/PSU matrix** confirms that the RTX 4090 is explicitly supported **only** when using the **1000W internal power supply**. Lower wattage PSUs (300W or 500W) are insufficient. ([developmentquote.monitors.com](https://developmentquote.monitors.com/shopify/product-pdf/4/precision-3680.pdf?utm_source=openai))  \n\n---\n\n### \ud83d\udd27 Key Fit & Power Requirements\n\nHere are the specific technical constraints to watch out for:\n\n| Specification | Value / Requirement |\n|---------------|---------------------|\n| Card Length | Up to **12.28 inches** (312.3 mm) for the RTX 4090 in Precision 3680 ([developmentquote.monitors.com](https://developmentquote.monitors.com/shopify/product-pdf/4/precision-3680.pdf?utm_source=openai)) |\n| Slot Width | The RTX 4090 is a **triple-wide** card \u2014 you'll need 3 adjacent slot bays free. ([developmentquote.monitors.com](https://developmentquote.monitors.com/shopify/product-pdf/4/precision-3680.pdf?utm_source=openai)) |\n| Power Connector | The 4090 uses a **16-pin** (12VHPWR / PCIe 5.0) power connector. Dell\u2019s variant for the 1000W builds supports this. ([developmentquote.monitors.com](https://developmentquote.monitors.com/shopify/product-pdf/4/precision-3680.pdf?utm_source=openai)) |\n| System Cooling & Airflow | With such a large GPU, make sure case airflow is unobstructed. The 3680 includes an air shroud and supports airflow improvements, especially in the 1000W configuration. ([delltechnologies.com](https://www.delltechnologies.com/asset/en-us/products/workstations/technical-support/precision-3680-spec-sheet.pdf?utm_source=openai)) |\n\n---\n\n### \u2757 Caveats\n\n- Even though the 1000W PSU is capable, 450W is the maximum supported card power draw for the GPU \u2014 this means an RTX 4090 that consumes up to ~450W under load should be fine. If you\u2019re using an overclocked version that draws significantly more than 450W, performance or stability issues may occur. ([dell.com](https://www.dell.com/en-us/shop/dell-pro-max-pcs-and-workstations/precision-3680-tower-workstation/spd/precision-t3680-workstation?utm_source=openai))  \n- Be sure the case layout doesn\u2019t block fan intake or exhaust with drive cages, cables, or other components.\n\n---\n\n### \ud83d\udca1 Bottom Line\n\nIf your Precision 3680 Tower has the **1000W internal PSU**, then yes \u2014 the RTX 4090 is a supported GPU option:\n\n- it fits within the length limit,\n- it has the needed power connector and slot space,\n- and it\u2019s listed in Dell\u2019s discrete GPU spec tables for that PSU configuration. ([dell.com](https://www.dell.com/support/manuals/en-us/oth-3680-xe/precision_3680_tower_om/gpudiscrete?guid=guid-1654b222-f3e6-448d-b304-ae6e792585aa&utm_source=openai))\n\nIf you want, I can double-check your exact 4090 model to make sure it meets all size and power limits \u2014 would you like me to do that?"
    },
    {
        "role": "user",
        "content": "give%20price%20range%20for%20a%203680%20with%2064gb%20ram%20and%20a%20rtx%204090"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a current pricing overview for a Dell Precision 3680 Tower workstation with 64 GB RAM + NVIDIA RTX 4090 (24 GB):\n\n---\n\n### \ud83d\udcb5 Recent Price Examples\n\n| Source | Condition | Specs Highlights* | Price |\n|---|---|---|---|\n| **eBay** (used/refurbished) | Certified refurbished \u201cgood/excellent\u201d | i9-14900 / 64 GB RAM / RTX 4090 24 GB / 2 TB SSD | **~ US $2,964** ([ebay.com](https://www.ebay.com/itm/177211780593?utm_source=openai)) |\n| **eBay** (sold) | Used/refurbished | i9-14900K / 64 GB RAM / RTX 4090 / 512 GB NVMe | **US $2,962** ([ebay.com](https://www.ebay.com/itm/387845776964?utm_source=openai)) |\n| **Monitors.com / EU site** | New (import) | i9-14900 / 64 GB / RTX 4090 / 1 TB NVMe | **\u20ac3,882 \u2013 \u20ac5,047** (~ US $4,200\u2013$5,500 depending on taxes/shipping) ([monitors.com](https://www.monitors.com/de-eu/products/dell-3680-core-i9-14900-64gb-rtx-4090?utm_source=openai)) |\n\n---\n\n### \ud83d\udd0d Estimated Price Range in the U.S.\n\nGiven those examples and factoring in new vs. used/refurbished, shipping, taxes, and possible vendor premiums:\n\n- **Refurbished / used**: Approx **US $2,900 to $3,300**\n- **New builds**: Likely **US $4,000 to $5,500+**, depending on configuration, storage capacity, and any additional features (warranty, OEM support, etc.)\n\n---\n\n### \u2699\ufe0f Important Variables\n\nThe actual price can swing a lot depending on:\n\n- CPU model (i9 vs. i7, \u201cK\u201d version vs regular)\n- Whether storage is 512 GB, 1TB, or 2TB (NVMe)\n- Type of warranty / support\n- New vs. refurbished condition\n- Availability and supply fluctuations (especially given recent demand for high-end GPUs)\n\n---\n\nIf you want, I can pull up current *Dell-official build-to-order* pricing to estimate exactly what a new Precision 3680 with 64 GB + RTX 4090 costs today."
    },
    {
        "role": "user",
        "content": "tell%20me%20about%20RTX%202000%20ada"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a detailed overview of the NVIDIA RTX 2000 Ada Generation GPU: what it is, its architecture, specs, strengths, and ideal use-cases.\n\n---\n\n## What is RTX 2000 Ada?\n\nThe **RTX 2000 Ada Generation** is NVIDIA\u2019s new small-form-factor (SFF) professional workstation GPU. Released in February 2024, it succeeds the RTX A2000 and is built on the Ada Lovelace architecture. It\u2019s designed for compact workstations, edge computing, AI-assisted workflows, and graphics-intensive tasks in constrained power and space environments. ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai))\n\n---\n\n## Architecture & Core Technologies\n\nThis GPU integrates several of NVIDIA\u2019s newer technologies:\n\n- **Ada Lovelace architecture**, which brings improvements in raw CUDA performance, ray tracing, and AI acceleration compared to previous generations. ([notebookcheck.net](https://www.notebookcheck.net/Nvidia-RTX-2000-Ada-combines-the-smallest-GeForce-RTX-4000-GPU-with-16-GB-VRAM-and-70W-TGP.802769.0.html?utm_source=openai))  \n- **3rd-generation RT (Ray Tracing) cores** \u2013 around 1.7\u00d7 faster RT performance than the preceding generation. ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai))  \n- **4th-generation Tensor Cores** \u2013 boost in AI/inference throughput, including formats like FP8. ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai))\n\n---\n\n## Key Specifications\n\nHere are the major specs for the RTX 2000 Ada:\n\n| Specification | RTX 2000 Ada |\n|----------------|----------------|\n| CUDA Cores | 2,816 ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n| RT Cores | 22 ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n| Tensor Cores | 88 ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n| Memory | 16 GB GDDR6 with ECC (error-correcting code) ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai)) |\n| Memory Interface | 128-bit, ~224 GB/s bandwidth ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n| Power Draw (TGP/TDP) | 70 W, no auxiliary power connector needed ([notebookcheck.net](https://www.notebookcheck.net/Nvidia-RTX-2000-Ada-combines-the-smallest-GeForce-RTX-4000-GPU-with-16-GB-VRAM-and-70W-TGP.802769.0.html?utm_source=openai)) |\n| Form Factor | Dual-slot, 6.6 inches / ~16.8 cm long; comes with both standard ATX and low-profile brackets ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n| Bus Interface | PCIe 4.0 \u00d78 (physically \u00d716 slot, electrically \u00d78) ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai)) |\n\n---\n\n## Performance & Use\n\n- Compared to the previous **A2000 (12GB)**, this card delivers ~**30\u201360%** better performance depending on workload, especially in graphics, virtualization, VR/AR design tools, video editing, and generative AI applications. ([crn.com](https://www.crn.com/news/components-peripherals/2024/nvidia-launches-follow-up-gpu-to-rtx-a2000-for-workstations-edge-computers?utm_source=openai))  \n- Ray-tracing and rendering workloads benefit significantly due to newer RT cores. ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai))  \n- AI-related tasks (for instance stable-diffusion image generation) perform noticeably better than older cards like the A2000. ([crn.com](https://www.crn.com/news/components-peripherals/2024/nvidia-launches-follow-up-gpu-to-rtx-a2000-for-workstations-edge-computers?utm_source=openai))\n\n---\n\n## Advantages (Pros)\n\n- High performance in tight power envelopes\u2014**70W power draw** allows operation in compact chassis without installing extra power cables. ([notebookcheck.net](https://www.notebookcheck.net/Nvidia-RTX-2000-Ada-combines-the-smallest-GeForce-RTX-4000-GPU-with-16-GB-VRAM-and-70W-TGP.802769.0.html?utm_source=openai))  \n- Strong memory capacity\u2014**16 GB** is generous for workloads involving large datasets or complex models. ECC support adds reliability. ([nvidia.com](https://www.nvidia.com/en-us/geforce/news/gfecnt/20242/rtx-2000-ada/?utm_source=openai))  \n- Physical flexibility\u2014comes with low-profile bracket option and dual-slot form factor, helpful for small workstations or rack-mount setups. ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai))\n\n---\n\n## Limitations & Considerations (Cons)\n\n- Though it performs well for its size and power, it **isn\u2019t in the same class** as high-end workstation or enthusiast cards (for example, RTX 4000 / 5000 Ada, RTX 4090, etc.). Heavy 4K gaming, large-scale rendering, or very large neural network training will still favor bigger, more powerful GPUs. ([notebookcheck.net](https://www.notebookcheck.net/NVIDIA-RTX-2000-Ada-Generation-Laptop-GPU-Benchmarks-and-Specs.744893.0.html?utm_source=openai))  \n- Performance depends on system\u2019s cooling capability and power delivery. Even though rated 70W, thermal throttling in small enclosures or poor airflow is possible. ([notebookcheck.net](https://www.notebookcheck.net/NVIDIA-RTX-2000-Ada-Generation-Laptop-GPU-Benchmarks-and-Specs.744893.0.html?utm_source=openai))  \n- Interface is PCIe 4.0 \u00d78, which is less than \u00d716 electrical bandwidth\u2014this may limit peak throughput slightly in heavily bandwidth-sensitive tasks. ([tomshardware.com](https://www.tomshardware.com/pc-components/gpus/nvidias-new-pro-gpu-costs-less-than-dollar650-rtx-2000-ada-generation-arrives-with-2816-cuda-cores-and-16gb-ecc-vram?utm_source=openai))  \n\n---\n\n## Bottom Line\n\nThe NVIDIA RTX 2000 Ada Generation is an excellent \u201csweet-spot\u201d workstation GPU if you need:\n\n- Decent pro graphics / visualization performance in a compact or lower-power build\n- 16 GB of trusted memory + Ada Lovelace\u2019s AI/RTX tech\n- Efficient performance without high power or large size\n\nIf you share your main use\u2014e.g., rendering, CAD, machine learning inference, or gaming\u2014I can help you determine if the RTX 2000 Ada is a fit, or whether a different card might suit your needs better."
    },
    {
        "role": "user",
        "content": "tell%20me%20about%20rtx%204060%2C%20how%20much%20memory"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a concise breakdown of the NVIDIA GeForce RTX 4060 \u2014 how much video memory it has, its specs, and what that means in practice:\n\n---\n\n### \ud83e\uddee Memory (VRAM)\n\n- The RTX 4060 comes with **8 GB of GDDR6** video memory. ([techspot.com](https://www.techspot.com/products/gpu/nvidia-geforce-rtx-4060.281792/?utm_source=openai))  \n- It uses a **128-bit memory interface**, which affects how fast it can transfer data to/from that VRAM. ([notebookcheck.net](https://www.notebookcheck.net/NVIDIA-GeForce-RTX-4060-GPU-Benchmarks-and-Specs.741899.0.html?utm_source=openai))  \n\n---\n\n### \ud83d\udd0d Key Specs & Features\n\n| Spec | Value |\n|------|-------|\n| CUDA Cores | 3,072 ([techspot.com](https://www.techspot.com/products/gpu/nvidia-geforce-rtx-4060.281792/?utm_source=openai)) |\n| Boost Clock | ~2.46 GHz (varies slightly by model) ([techspot.com](https://www.techspot.com/products/gpu/nvidia-geforce-rtx-4060.281792/?utm_source=openai)) |\n| Total Board Power | ~115 W ([notebookcheck.net](https://www.notebookcheck.net/NVIDIA-GeForce-RTX-4060-GPU-Benchmarks-and-Specs.741899.0.html?utm_source=openai)) |\n| Memory Bandwidth | ~272 GB/s ([notebookcheck.net](https://www.notebookcheck.net/NVIDIA-GeForce-RTX-4060-GPU-Benchmarks-and-Specs.741899.0.html?utm_source=openai)) |\n\n---\n\n### \ud83e\udde0 Practical Implications\n\n- **1080p gaming**: The 8 GB should handle most titles well at 1080p with max or near-max settings. Ray tracing and very high-resolution textures might push VRAM usage up, so you might need to tweak a few settings (like texture quality). ([tomshardware.com](https://www.tomshardware.com/reviews/nvidia-geforce-rtx-4060-review-asus-dual/2?utm_source=openai))  \n- **1440p or ultra/high-res work**: Less ideal. The smaller memory capacity and bandwidth can limit performance, especially with demanding visual settings or games that use a lot of VRAM. ([arstechnica.com](https://arstechnica.com/gadgets/2023/06/geforce-rtx-4060-review-not-thrilling-but-a-super-efficient-299-workhorse/?utm_source=openai))  \n- **Other uses** (content creation, AI inference, etc.): Depends on workload. For lighter tasks or where you\u2019re not rendering huge scenes or models, the 8 GB could be fine. For heavier workloads, more VRAM is definitely better.\n\n---\n\nIf you want, I can pull up comparisons showing how the RTX 4060 performs vs. similar cards with more memory (like the RTX 3060 12GB) to help you decide if 8 GB will be enough for what you plan to do."
    },
    {
        "role": "user",
        "content": "if%20i%20have%204%20of%20them%2C%20can%20i%20run%20models%20as%20if%20i%20had%20a%2032gb%20gpu%20or%2024gb%20gpu%3F"
    },
    {
        "role": "assistant",
        "content": "Having 4\u00d7 RTX 4060 cards gives you substantial GPU compute power, but it doesn't work like having one GPU with the combined VRAM (e.g., 4\u00d78 GB \u2260 32 GB in the sense of a single monolithic memory pool). Here\u2019s how it works, what\u2019s possible, and the limits:\n\n---\n\n## \u2699\ufe0f How Multi-GPU Memory Works\n\n1. **Memory Is Not Automatically Combined**  \n   Each GPU has its own distinct VRAM. There's no automatic pooling or shared frame buffer across consumer cards that would let a single model access all 32 GB as if in one contiguous block. Even NVLink on consumer GPUs typically doesn\u2019t give true unified memory you can allocate across all cards as one chunk. ([blender.stackexchange.com](https://blender.stackexchange.com/questions/195363/is-pooling-vram-with-nvidia-nv-link-actually-possible?utm_source=openai))\n\n2. **You Can Use Multiple GPUs via Parallelism**  \n   You can split work across multiple cards in a few standard ways:  \n\n   - **Data Parallelism (DP / DDP)** \u2014 Each GPU holds a full copy of the model. They each process a slice of the input batch, then gradients are synced. Memory per GPU still needs to accommodate the entire model. ([huggingface.co](https://huggingface.co/docs/transformers/v4.50.0/perf_train_gpu_many?utm_source=openai))  \n   - **Model Parallelism / Layer Partitioning** \u2014 Split parts of the model (e.g. layers) across GPUs. Some layers live on GPU1, others on GPU2, etc. During forward/backward passes data is sent between GPUs. This can allow handling larger models than any single GPU\u2019s VRAM, but you're constrained by each chunk of the model fitting in that card\u2019s memory. ([pytorch-lightning.readthedocs.io](https://pytorch-lightning.readthedocs.io/en/2.1.4/fabric/advanced/model_parallel/fsdp.html?utm_source=openai))  \n   - **Hybrid / Advanced Methods** \u2014 Frameworks like DeepSpeed ZeRO, PyTorch Fully Sharded Data Parallel (FSDP), or sharded data parallelism split not only the data but optimizer state, activations, etc., across GPUs to reduce memory load per card. These let you handle larger models or larger batch sizes than with naive data parallelism. ([docs.aws.amazon.com](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html?utm_source=openai))\n\n---\n\n## \ud83d\udd0d Can 4\u00d7 RTX 4060 Be Used Like a 32 GB (or 24 GB) GPU?\n\nIt depends on what you mean by \"as if\":\n\n- **24-30 GB Single-GPU Equivalent?**  \n  If your workload is exactly at the edge of what 24\u201330 GB can support *and* the model architecture is nicely splittable (e.g. each layer or chunk fits within 8 GB), plus you use model parallelism / sharding, then yes \u2014 you might be able to run something that a single 24 GB GPU could, but possibly with more complexity and overhead.\n\n- **True 32 GB Equivalent?**  \n  Not directly: you can\u2019t allocate a single tensor of size that requires more than 8 GB VRAM on one card and expect it to spill over into other GPUs unless your framework supports that explicitly (which is relatively rare and less efficient). Many libraries require individual layers (and activations) to fit in a single card\u2019s memory. ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1hxosc8?utm_source=openai))\n\n- **Batch Size vs. Model Size**  \n  More GPUs help more with increasing batch size (data parallelism) rather than increasing maximum model or activation size (unless using model parallelism or sharding). If your model is just barely too big for one 8 GB card, splitting it might help, but you\u2019ll incur communication overhead.\n\n---\n\n## \u2705 What to Do to Maximize Use of Multiple 8 GB GPUs\n\nIf you want to approximate having a larger-VRAM GPU using 4\u00d7 RTX 4060, here are good strategies:\n\n- Use frameworks/tools like **DeepSpeed**, **FairScale**, **FSDP** (PyTorch), or **Deepspeed ZeRO** that support optimizer/gradient/parameter sharding.  \n- Use **Model Parallelism**: partition layers or blocks of the model across GPUs.  \n- Try **activation checkpointing** to free up memory during forward pass, trading compute for memory.  \n- Reduce precision (FP16 / BFLOAT16 / mixed precision) where supported.  \n- Keep your layer sizes or intermediate tensor activations aligned so no single chunk exceeds ~8 GB on a card.\n\n---\n\n## \u26a0\ufe0f Downsides & Overheads\n\n- **Communication overhead** between cards slows things down. Using PCIe is slower than NVLink; the more split/parallel your workloads, the more inter-GPU traffic.  \n- **Memory fragmentation** can waste VRAM.  \n- **Implementation complexity** increases: debugging model parallelism can be harder than running on a single GPU.  \n- **Performance penalties**: model parallel or sharded setups usually have some slowdown vs ideal single-GPU case.\n\n---\n\n## \ud83d\udd1a Bottom Line\n\nYou *can* approximate having a larger-memory GPU using 4 RTX 4060s, but not exactly. For many workloads (especially large-batch training, inference, or models that can be split), it can work well. But for workloads needing large tensors or layers that each exceed ~8 GB, you\u2019ll still hit limits, or need to break them apart.\n\nIf you tell me exactly what model or workload you\u2019re thinking of (e.g. Stable Diffusion, LLaMA, training vs inference, etc.), I can give you a more precise estimate of whether 4\u00d7 4060 will be enough, or whether you\u2019ll run into trouble."
    },
    {
        "role": "user",
        "content": "how%20does%20it%20compare%20to%20rtx%204000%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a detailed comparison between the **RTX 4060** and the **RTX 4000 Ada Generation** \u2014 highlighting their similarities, differences, and what those mean depending on how you plan to use them.\n\n---\n\n## \ud83c\udfaf What the Cards Are Aimed At\n\n- **RTX 4060**: Geared toward mainstream gamers and lighter compute tasks. Delivers solid performance in 1080p gaming and works fine for smaller models/inference or entry-level content creation. ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai))  \n- **RTX 4000 Ada Generation**: A workstation-class card with pro graphics/driving capabilities. Meant for designers, creators, engineers, and people needing larger VRAM and stability for professional applications. Big in AI / ML training or inference where memory and throughput matter. ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai))  \n\n---\n\n## \ud83d\udcca Spec Comparison\n\n| Specification | RTX 4060 | RTX 4000 Ada |\n|---------------|-----------|---------------|\n| VRAM | 8 GB GDDR6 ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai)) | 20 GB GDDR6 ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) |\n| Memory Bus / Interface | 128-bit ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) | 160-bit ([technical.city](https://technical.city/en/video/GeForce-RTX-4060-vs-RTX-4000-Ada-Generation?utm_source=openai)) |\n| Memory Bandwidth | ~272 GB/s ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) | ~360 GB/s ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) |\n| CUDA Cores / Shaders | 3,072 ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai)) | 6,144 ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai)) |\n| Boost Clock | ~2,460 MHz ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) | ~2,175 MHz ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) |\n| Base Clock | ~1,830 MHz ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) | ~1,500 MHz ([gpu-monkey.com](https://www.gpu-monkey.com/en/compare_gpu-nvidia_rtx_4000_ada-vs-nvidia_geforce_rtx_4060?utm_source=openai)) |\n| Power Draw (TGP/TDP) | ~115 W ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) | ~130 W ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) |\n| FP32 / FP16 Throughput (Compute Performance) | ~15.1 TFLOPS ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) | ~26.7 TFLOPS ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai)) |\n\n---\n\n## \u26a1 Performance Differences & Implications\n\n- **VRAM capacity advantage**: The 4000 Ada has **20 GB vs 8 GB**. This matters significantly for large ML models, high-resolution rendering, and complex workloads where you need more memory to avoid crashes or memory bottlenecks. ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai))  \n- **Compute power**: With nearly double the CUDA cores/shaders and more tensor/RT cores, the 4000 Ada can execute AI inference/training much faster, handle larger batch sizes, and produce higher FPS (especially at 1440p or in ray-traced settings). ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai))  \n- **Memory bandwidth**: The 4000 Ada\u2019s higher bandwidth (~360 GB/s) gives it better throughput for memory-heavy tasks (large textures, large activations, etc.), whereas the 4060\u2019s narrower bandwidth can become a limiting factor when you're editing 4K video, dealing with large ML tensors, or rendering complex scenes. ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai))  \n- **Efficiency & power draw**: The 4060, with its lower TDP, is more efficient in terms of watts per TFLOPS. It\u2019s better for energy-sensitive builds or setups with cooling constraints. ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai))  \n\n---\n\n## \ud83d\udee0 Real-World Use & What\u2019s Possible\n\n- **Gaming**: If you're mostly gaming at 1080p or even 1440p, the 4060 may be totally adequate. The 4000 Ada will perform better at higher detail levels, ray tracing, and demanding frame rates at 1440p or 4K. ([nanoreview.net](https://nanoreview.net/en/gpu-compare/nvidia-rtx-4000-ada-vs-geforce-rtx-4060?utm_source=openai))  \n- **ML / AI / Inference**: For large models, multiple GPUs, or batch-heavy workloads, the 4000 Ada is a clearer winner. More VRAM allows for bigger models and more room for model parallelism. The 4060 is OK for smaller local models or lighter inference tasks. ([gpuperhour.com](https://gpuperhour.com/compare/rtx-4000-ada-vs-rtx-4060?utm_source=openai))  \n- **Content Creation & Graphics**: If you render or edit high-resolution video, use large textures, or work in 3D/CAD, the 4000 Ada\u2019s extra memory, more RT / Tensor cores, and greater bandwidth will make a noticeable difference. The 4060 works, but you\u2019ll hit limits sooner (when textures/frames get large, or with multiple layers/effects).  \n\n---\n\nIf you tell me *your* setup (how you use your GPU, type of models, gaming vs rendering, etc.), I can help estimate whether the difference is worth the upgrade for *your* use case."
    },
    {
        "role": "user",
        "content": "which%20open%20source%20models%20run%20well%20on%20the%20RTX%204000%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a breakdown of open-source models that run well on the **RTX 4000 Ada Generation (20 GB VRAM)**, what quantization you\u2019ll likely need, and what performance to expect.\n\n---\n\n## \ud83d\udd0d What Makes the RTX 4000 Ada Ability Strong\n\n- It offers **20 GB of ECC GDDR6**, which gives you room to run 13B parameter models at FP16 or 20\u201330B models when quantizing. ([willitrunai.com](https://willitrunai.com/gpus/rtx-4000-ada-20gb?utm_source=openai))  \n- Supports lower-precision formats like **FP8**, **INT8**, and **INT4**, which help bring large models into the realm of what 20 GB GPUs can handle. ([inferencebench.io](https://inferencebench.io/models/sensetime/internlm-20b/?utm_source=openai))  \n\n---\n\n## \u2705 Models That Fit Well on 20 GB\n\nHere are specific models that run well, either out of the box or with quantization/other tweaks:\n\n| Model | Param Count / Type | What\u2019s Needed on RTX 4000 Ada | Expected Performance |\n|-------|----------------------|-------------------------------|-----------------------|\n| **Llama-2 7B (\u201cChat\u201d and others)** | 7B parameters | With Q4_K_M quantization, uses ~8\u20139 GB VRAM. Plenty of headroom. ([willitrunai.com](https://willitrunai.com/can-run/hf-thebloke--llama-2-7b-chat-gguf-on-rtx-4000-ada-20gb?utm_source=openai)) | Can see ~60+ tokens/sec decoding; great for chat, coding, etc. ([willitrunai.com](https://willitrunai.com/can-run/hf-thebloke--llama-2-7b-chat-gguf-on-rtx-4000-ada-20gb?utm_source=openai)) |\n| **GPT-OSS 20B** | 20-21B MoE model, 3.6B parameters active | Needs about 13\u201315 GB VRAM under Q4_K_M; fits inside 20 GB with good margin. ([fitmyllm.com](https://www.fitmyllm.com/model/gpt-oss-20b?utm_source=openai)) | Solid throughput (~50\u201365 tok/sec); headroom for context window without huge slowdowns. ([vram.run](https://vram.run/hw/rtx_4000_sff_ada_generation/?utm_source=openai)) |\n| **Mistral-Small-24B** | 24B dense model | Tight at ~15 GB using quantization; more context or FP16 might push you over. ([vram.run](https://vram.run/hw/rtx_4000_sff_ada_generation/?utm_source=openai)) | Slower decoding (10\u201320 tok/sec), but usable for experiments or lighter tasks. |\n| **Llama-3.1-8B** | 8B | Fits comfortably \u2014 under 4\u20135 GB with low-precision quant; plenty of room. ([vram.run](https://vram.run/hw/rtx_4000_sff_ada_generation/?utm_source=openai)) | Responsive and fast inference; good choice if you want smooth interactions with some context. |\n\n---\n\n## \u26a0\ufe0f Models That Are Tough / Need Tricks\n\nSome models either won\u2019t fit or will be sluggish unless you aggressively quantize or use offloading to CPU memory:\n\n- **GPT-NeoX-20B** requires ~40\u201345 GB VRAM at FP16, so it\u2019s too big for 20 GB without heavy optimization. ([bacloud.com](https://www.bacloud.com/en/blog/163/guide-to-gpu-requirements-for-running-ai-models.html?utm_source=openai))  \n- Dense models in the 30B+ parameter range are possible only if quantized to INT4 or similar and with reduced context length. Even then, speed drops. ([vram.run](https://vram.run/hw/rtx_4000_sff_ada_generation/?utm_source=openai))  \n\n---\n\n## \u2699\ufe0f Tips to Maximize What Fits\n\nIf you're working with a 20 GB GPU, here are helpful strategies to squeeze more in:\n\n- Use **quantization** (INT4, INT8, Q2, Q3_K_M, etc.) to reduce VRAM significantly.  \n- Keep batch size small\u2014often batch size = 1 for inference.  \n- Limit context size (number of prompt tokens) where possible\u2014long contexts add KV cache memory usage.  \n- Use frameworks that support memory offloading (moving some parts to CPU) or tensor/model parallelism if needed.  \n- Choose \u201cflash attention\u201d or \u201cefficient transformers\u201d versions that optimize memory usage during the attention phase.\n\n---\n\nIf you tell me *exactly* the model or use-case you have in mind (e.g., coding assistant, chat, image generation, etc.), I can map it more precisely to whether 20 GB will be enough."
    },
    {
        "role": "user",
        "content": "which%20models%20run%20well%20for%20coding%20tasks%20like%20python%20on%20the%20rtx%204000%3F"
    },
    {
        "role": "assistant",
        "content": "If you're focused on Python coding tasks and planning to use a GPU like the RTX 4000 Ada (\u2248 20 GB of VRAM), here are some open-source models that perform well, plus tips for running them efficiently and what tradeoffs to expect.\n\n---\n\n## \u2705 Top Models for Coding (Python, Autocomplete, Debugging, etc.)\n\nHere are several models that are strong choices for Python-specific code work:\n\n| Model | Size / How It\u2019s Used | Why It\u2019s Good for Python & Coding Tasks |\n|---|-------------------------|-------------------------------------------|\n| **Code Llama 7B (Python variant)** | 7 billion parameters, optimized for code/data science tasks | Performs very well on benchmarks like HumanEval and MBPP. Light enough to run comfortably on a 20 GB GPU with low-precision quantization. ([arxiv.org](https://arxiv.org/abs/2308.12950?utm_source=openai)) |\n| **StarCoder-7B** | ~7 B, supports many languages | Broad language support and good completion quality. Typically fits into ~8-10 GB VRAM. ([localaiops.com](https://localaiops.com/posts/tabby-self-hosted-code-completion-with-local-models/?utm_source=openai)) |\n| **DeepSeek Coder V2 (16B variant)** | 16 B params \u2014 but with quantization down to ~Q4 can fit under 20 GB for inference | High Python/joint code-language performance; good if you want stronger accuracy and are OK with somewhat higher latency. ([runyard.dev](https://www.runyard.dev/blog/best-local-llms-for-coding-2026?utm_source=openai)) |\n| **Granite-20B-Code-Instruct-8K** | 20B parameter model fine-tuned for instruction-following and code tasks, 8K context length | Great for more complex prompting, explanations, debugging, and multi-step instructions; pushing the limit of what 20 GB can handle \u2014 needs quantization and maybe mixed CPU/GPU use. ([huggingface.co](https://huggingface.co/ibm-granite/granite-20b-code-instruct/?utm_source=openai)) |\n| **gpt-oss-20B (OpenAI\u2019s open-weight)** | 21B params, mixture\u2010of\u2010experts (MoE) architecture | On coding benchmarks, comparable to strong closed-source models. Supports coding + reasoning, fits more comfortably than you might expect with MoE activation sparsity. ([tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/openai-intros-two-lightweight-open-model-language-models-that-can-run-on-consumer-gpus-optimized-to-run-on-devices-with-just-16gb-of-memory?utm_source=openai)) |\n\n---\n\n## \u2699\ufe0f Running on RTX 4000 Ada \u2014 Tips & Tricks\n\nTo get decent performance and usability, you\u2019ll want to use certain techniques:\n\n- **Quantization**: Use formats like Q4_K_M, INT4, or similar. These vastly reduce the VRAM footprint with modest quality loss. Most of the models above offer quantized versions. ([lsuse.com](https://lsuse.com/post/setting-up-local-ollama-with-opencode-on-16gb-vram?utm_source=openai))  \n- **Small batch size** or *batch size = 1* for inference. Python completions are interactive; users usually prefer quick, short responses.  \n- **Reasonable context lengths**: 4\u20138K tokens are usually safe; going to 16K+ strongly increases memory usage.  \n- **Use CPU offloading** or frameworks that support splitting unneeded data/activations onto system RAM if VRAM runs out (e.g. with DeepSeek Coder V2 on 20 GB, you can run at Q4 quant but might need to offload some stuff). ([lsuse.com](https://lsuse.com/post/setting-up-local-ollama-with-opencode-on-16gb-vram?utm_source=openai))  \n- **Lightweight models for speed**: If you\u2019re coding and want snappy autocomplete, 7B-class models (Code Llama 7B, StarCoder 7B) are often the best balance between speed and accuracy. For heavy tasks (multi-file, deep debugging, large context), 16B or 20B might provide better results at cost of latency.  \n\n---\n\n## \u26a0\ufe0f Trade-Offs & What to Watch Out For\n\n- **Latency**: Heavier models (16\u201320B) or long contexts + quantization can slow down autocomplete and responsiveness. If you want lag-free coding, lean smaller.  \n- **Accuracy vs Size**: Smaller models might make more syntax or logic mistakes. For critical code, you\u2019ll want stronger models or maybe ensembles / checks.  \n- **Context & Memory**: More context = more VRAM usage. Code models need to carry over variables, imported libraries, indentation, etc., which adds to prompt size.  \n- **Fine-tuning & Domain**: If your Python work includes specialized libraries (e.g. scientific stack, web dev frameworks), fine-tuned models or prompt-tuning will help.\n\n---\n\nIf you like, I can compare a specific pair of models (say `Code Llama 7B` vs `DeepSeek Coder V2` or something) for your kind of Python workflow (e.g. scripting, data science, web dev). Want me to do that?"
    },
    {
        "role": "user",
        "content": "how%20much%20ram%20rtx%205080%3F"
    },
    {
        "role": "assistant",
        "content": "The **GeForce RTX 5080** comes with **16 GB of VRAM**, using the new GDDR7 memory. ([notebookcheck.net](https://www.notebookcheck.net/Nvidia-GeForce-RTX-5080-Benchmarks-and-Specs.935679.0.html?utm_source=openai))\n\nIf you want more details about that VRAM (e.g. speed or bandwidth), I can dig those up too."
    }
]