[
    {
        "role": "user",
        "content": "what%20are%20users%20on%20reddit%20and%20elsewhere%20saying%20about%20framework%20desktop%20pc%20for%20ai%20vs%20mac%20or%20nvidia%20spark"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a summary of what Redditors and tech reviewers are saying about the **Framework Desktop PC for AI** compared to **Macs** (especially Apple Silicon Macs like Mac Studio / MacBook Pro) and **NVIDIA\u2019s DGX Spark**. I pulled from recent posts (2024-2026) to reflect the current sentiment.\n\n---\n\n## Hardware / Performance Comparison\n\n### Framework Desktop Highlights\n\nBased on reviews:\n\n- The Framework Desktop uses **AMD Ryzen AI Max** (\u201cStrix Halo\u201d) chips (e.g. Max 385 or Max+ 395) and offers configurations up to **128 GB soldered RAM**. ([pcworld.com](https://www.pcworld.com/article/2866400/framework-desktop-review.html?utm_source=openai))  \n- It delivers strong performance for local AI inference (especially smaller or efficient models), and solid productivity and even light gaming. ([pcworld.com](https://www.pcworld.com/article/2866400/framework-desktop-review.html?utm_source=openai))  \n- Physical form factor is compact (around 4.5 L) and relatively quiet. However, RAM is not user-upgradable as it\u2019s soldered. ([phoronix.com](https://www.phoronix.com/review/framework-desktop-linux?utm_source=openai))\n\n### DGX Spark\n\n- NVIDIA\u2019s DGX Spark boasts **128 GB unified memory** and can run reasonably large models locally (e.g. Llama 3.1 70B, Gemma 3 27B). ([techradar.com](https://www.techradar.com/pro/so-freaking-cool-first-reviews-of-nvidia-dgx-spark-leave-absolutely-no-doubt-this-may-be-nvidias-apple-mac-moment?utm_source=openai))  \n- Strong hardware but limited memory bandwidth has been noted as a bottleneck in some benchmarks. ([techradar.com](https://www.techradar.com/pro/so-freaking-cool-first-reviews-of-nvidia-dgx-spark-leave-absolutely-no-doubt-this-may-be-nvidias-apple-mac-moment?utm_source=openai))  \n\n### Apple Macs\n\n- Apple Silicon Macs with large unified memory (64\u2013512 GB) get praise for their **memory bandwidth** and ability to run sizable models efficiently. ([reddit.com](https://www.reddit.com/r/generativeAI/comments/1rf9nh5/mac_or_pc/?utm_source=openai))  \n- For inference/local AI work, Macs are often considered \u201csilent, efficient, and good value per GB of usable model memory.\u201d But for training or highly parallel workloads, NVIDIA GPUs still usually outperform. ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/?utm_source=openai))\n\n---\n\n## What the Community Likes / Dislikes\n\n### Framework Desktop: Pros\n\n- **Cost-to-memory ratio**: For people wanting a machine to run local LLMs, the Framework Desktop offers large memory (128 GB) for less money than equivalent Mac Studio configurations. ([reddit.com](https://www.reddit.com/r/LocalLLM/comments/1iyhz3j?utm_source=openai))  \n- **Flexibility & openness**: Users appreciate being able to use non-Apple ecosystems, open toolchains, Linux, etc. Some dislike Apple\u2019s tighter controls. ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/?utm_source=openai))  \n\n### Framework Desktop: Cons / Trade-offs\n\n- **Soldered RAM**: Once you buy it, you\u2019re stuck with whatever memory configuration you chose. ([pcworld.com](https://www.pcworld.com/article/2866400/framework-desktop-review.html?utm_source=openai))  \n- **Performance vs cost**: For purely gaming or GPU-intensive workloads, some say you can build a cheaper PC with discrete GPU delivering higher FPS. In AI workloads, the gains are more mixed and depend a lot on how well the software / drivers (e.g. Vulkan) are optimized. ([pcgamer.com](https://www.pcgamer.com/hardware/gaming-pcs/framework-desktop-pc-review/?utm_source=openai))  \n\n---\n\n## Framework vs DGX Spark vs Mac \u2014 Community Takes\n\nHere\u2019s how folks rank them in different scenarios:\n\n| Use Case | Favor Framework Desktop | Favor DGX Spark | Favor Apple Mac |\n|---|---|---|---|\n| **Local inference for small-to\u2010mid LLMs** (e.g. 20-70B parameters) | Often seen as good value, powerful enough, especially with 64-128 GB RAM. ([reddit.com](https://www.reddit.com/r/framework/comments/1t6705y/framework_desktop_vs_mac_studio_m3_ultra_vs_dgx/?utm_source=openai)) | Stronger for users already committed to NVIDIA ecosystem, or those needing CUDA / enterprise support. ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/?utm_source=openai)) | Macs perform well here when memory resources are large and tools are optimized. Strong options especially for inference rather than training. ([reddit.com](https://www.reddit.com/r/generativeAI/comments/1rf9nh5/mac_or_pc/?utm_source=openai)) |\n| **Training or fine-tuning large models** | Less optimal: fewer cores; integrated GPU; limited bandwidth. Good for some lighter fine-tuning or MoE (Mixture of Experts) models. ([reddit.com](https://www.reddit.com/r/framework/comments/1swqvn2/local_ai_on_framework_desktop/?utm_source=openai)) | Better for GPU-heavy training workloads especially with NVIDIA\u2019s software stack. But pricey. ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/?utm_source=openai)) | Macs still lag in training speed vs discrete GPUs/NVIDIA setups, especially on very large models. |\n| **Ecosystem & usability** | Pro: open, Linux friendly, flexibility; Con: sometimes driver / optimization gaps. ([phoronix.com](https://www.phoronix.com/review/framework-desktop-linux?utm_source=openai)) | Pro: ecosystem strength (CUDA, NVIDIA tools); Con: cost, sometimes proprietary limitations. ([reddit.com](https://www.reddit.com/r/ollama/comments/1o6oo5y/nvidia_dgx_spark_is_it_worth/?utm_source=openai)) | Pro: tight integration, low noise/heat; Con: cost, Apple-only tools, less flexibility with GPUs. |\n\n---\n\n## Key Quotes from Reddit\n\n- Someone using the Framework Desktop with 128 GB unified memory wrote: \u201cThe desktop seems like an incredibly solid piece of hardware \u2026 I was looking at NVIDIA Spark \u2026 but those seem to have issues \u2026 because it\u2019s ARM I can\u2019t run MATLAB or Simulink on it.\u201d ([reddit.com](https://www.reddit.com/r/framework/comments/1swqvn2/local_ai_on_framework_desktop/?utm_source=openai))  \n- Another said: \u201cDGX Spark\u2019s inference performance is a little bit disappointing \u2026 for a user that just wants to run local models, I find the Framework Desktop **cheaper and quite interesting** \u2026 I find the Mac Studio or some MacBook Pro M4 Max even more interesting to run big models with a good token/s performance.\u201d ([reddit.com](https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/?utm_source=openai))  \n- A comment on value: \u201cNow, apart from having to deal with Apple stuff \u2026 the $/GB for local LLM makes a used 512 GB Mac studio seem like a steal over 3\u00d7 Framework Desktops \u2026 Forget DGX Spark \u2026 VRAM is king and if you\u2019re able to get 512 GB of VRAM for 3k\u2026?\u201d ([reddit.com](https://www.reddit.com/r/framework/comments/1t6705y/framework_desktop_vs_mac_studio_m3_ultra_vs_dgx/?utm_source=openai))  \n\n---\n\n## Verdict: Which is Better in What Scenario\n\nPutting it all together:\n\n- If your priority is **best performance per dollar for local inference** and mid-sized models, **Framework Desktop** is a compelling option. It gives a lot of RAM, good CPU/GPU, and open software flexibility.\n- If you already rely heavily on **NVIDIA tools**, CUDA, or need larger model training/fine-tuning capabilities, the **Spark** or other NVIDIA-based solutions are stronger.\n- If you want a mostly silent, stable, efficient machine, especially for inference and macOS-native tools, **Apple Silicon Macs** still shine in terms of memory bandwidth, integration, and (for some) lower power draw.\n\n---\n\nIf you'd like, I can pull up recent benchmarks (for example, token per second for large models) comparing these directly, to help you decide based on your specific workflow. Want me to do that?"
    },
    {
        "role": "user",
        "content": "ok%2C%20what%20are%20some%20beefy%20mac%20models%20that%20people%20are%20using%2C%20how%20much%20ram%2C%20and%20how%20much%20might%20we%20expect%20on%20the%20secondary%20market%20like%20ebay%20for%20a%20slightly%20older%20m%20series%3F"
    },
    {
        "role": "assistant",
        "content": "Here\u2019s a breakdown of some \u201cbeefy\u201d Mac models people are using for AI / LLM work, how much unified memory they typically carry, and what kind of prices they tend to fetch on the secondary market (eBay & similar) \u2014 especially for slightly older / refurbished units. Data is current as of mid-2026, U.S. prices unless otherwise noted.\n\n---\n\n## Mac Models & RAM Configurations Used in AI / Local LLMs\n\nThese are the Mac models folks tend to gravitate toward when doing serious local inference.\n\n| Model                             | Unified Memory (UBX)         | Typical Use Cases / What Kind of Models It Handles               |\n|-----------------------------------|-------------------------------|------------------------------------------------------------------|\n| **Mac Studio (M4 Max)**           | 128 GB (maxed)               | 70B+ parameter models (Llama 3.1, Mixtral, etc.), full local AI workflows ([awesomeagents.ai](https://awesomeagents.ai/hardware/apple-m4-max/?utm_source=openai)) |\n| **Mac Studio (M3 Ultra)**         | Up to 512 GB                 | Very large research-grade models (DeepSeek R1 671B shown) plus serious multi-agent or inference serving setups ([techradar.com](https://www.techradar.com/pro/apple-mac-studio-m3-ultra-workstation-can-run-deepseek-r1-671b-ai-model-entirely-in-memory-using-less-than-200w-reviewer-finds?utm_source=openai)) |\n| **Mac Mini (M4 Pro)**             | 24 GB, 48 GB (some variant 64GB) | Sweet spot: 14B\u201332B parameter models with decent performance, especially at quantized precision ([insiderllm.com](https://insiderllm.com/pdfs/mac-mini-m4-local-ai.pdf?utm_source=openai)) |\n| **MacBook Pro (M3 or M4 Max/Pro)** | 32 GB, 48 GB, 64 GB, 128 GB  | Portable inference, smaller models (8B-32B); more costly for 70B+ models but usable ([refurb.me](https://www.refurb.me/blog/best-mac-for-ai?utm_source=openai)) |\n\nUniversal consensus: More RAM helps a lot. Memory bandwidth too (so the \u201cMax\u201d / \u201cUltra\u201d chips with high bandwidth are preferred). And quantization (e.g. Q4_K_M etc.) lets larger models be viable even on 48-64 GB machines. ([llmhardware.io](https://llmhardware.io/guides/mac-mini-m4-llm-guide?utm_source=openai))\n\n---\n\n## What Models Can You Run vs How Much Ram\n\nSome rule-of-thumb RAM vs model size:\n\n- 16-24 GB unified RAM: good for ~7B-14B parameter models, quantized (Q4), maybe small context windows. ([llmhardware.io](https://llmhardware.io/guides/mac-mini-m4-llm-guide?utm_source=openai))  \n- 32-48 GB: comfortably handle 20-30B models, some 32B with quantization. ([insiderllm.com](https://insiderllm.com/pdfs/mac-mini-m4-local-ai.pdf?utm_source=openai))  \n- 64 GB+: useful for 70B models (quantized) or fine-tuning / more aggressive context sizes. ([awesomeagents.ai](https://awesomeagents.ai/hardware/apple-m4-max/?utm_source=openai))  \n- 128 GB / 192 GB / 512 GB: for pushing the envelope \u2014 huge models, long contexts, possibly running \u201cultra\u201d models entirely in memory. ([awesomeagents.ai](https://awesomeagents.ai/hardware/apple-m4-max/?utm_source=openai))  \n\n---\n\n## Secondary Market Prices (eBay etc.) for Slightly Older / Refurb Macs\n\nHere are real examples of used/refurb Apple Silicon Macs with ~\u201cbeefy enough\u201d memory for AI, with their sale prices / listings:\n\n| Model & RAM              | Price (Used / Refurbished)                  | Notes                                                                 |\n|--------------------------|----------------------------------------------|------------------------------------------------------------------------|\n| Mac Mini (2024) M4 Pro, **48 GB** RAM, 1TB SSD | ~$1,899 ([ebay.com](https://www.ebay.com/itm/306840941381?utm_source=openai)) | Recently sold used; solid performance for ~32 B models with quantization. |\n| Mac Studio (2022) M1 Max, **64 GB** RAM, 1 TB SSD | ~$1,700 ([ebay.it](https://www.ebay.it/itm/364506290685?utm_source=openai)) | Fairly common in used/refurb channels; good bang-for-buck vs new high-memory Macs. |\n| Mac Studio (2022) M1 Max, **64 GB** RAM, 2 TB SSD | ~$1,425 ([ebay.es](https://www.ebay.es/itm/127610642443?itmmeta=01KEEWSCECKRCY0M7A3NWKPX3P&itmprp=enc%3AAQAKAAAA4NHOg0D50eDiCdi%2FfP0r02uDjgPpgEg7jxK%2BTKh7OCW9gaZ9Q19oEfkse3BH0Qi3mkrrr0HnQ4DxpR%2Fm4JFJKQQmVI9eTgCAfyzRhfMrhJWtdd6TZDd3ELRK8coQfHuEqWeeWBKmYBR%2BppD3nCgbeb--pOIGW5fco%2B4dX3gVJLNh8gyWZOnokkSl22x%2BzKO9g%2FLo0Xd0PeBswhEXwt3JIYvNkH5BSd2z0veD8bKd8coQe3obmCtdr0RlmPPLfOrSKh02ScrXPXFMoN5R5ro8C1xGeGJSqEANJMr8MpM%2FENKk%7Ctkp%3ABk9SR67H5dzzZg&utm_source=openai)) | Slightly larger SSD tends to push price higher; condition matters. |\n| Mid-Tier MacBook Pro M3 Max / M3 Pro (~48-64 GB) | Varies (often $2,500-$3,000 refurb depending on config) | Battery health, GPU cores, screen size, and condition drive price. (refs: reddit-quoted) ([reddit.com](https://www.reddit.com/r/applehelp/comments/1hckfu3?utm_source=openai)) |\n\nAlso: used base/M1 Max Mac Studios with **64GB RAM** usually show up between **$1,300 to $1,700** depending on condition, SSD size, etc. ([ebay.it](https://www.ebay.it/itm/364506290685?utm_source=openai))\n\nMac Mini M4 Pro 48 GB units tend to hover around **$1,800\u2010$2,000** used/sold listings. ([ebay.at](https://www.ebay.at/itm/198167403667?utm_source=openai))\n\n---\n\n## What This Means If You\u2019re Shopping Refurbished / Used\n\nIf you want something \u201cbeefy enough\u201d for solid local AI work without paying flagship new prices:\n\n- **Aim for at least 48 GB** unified memory, ideally 64 GB or more if you\u2019ll run models ~30-70B with decent performance.  \n- **Mac Mini M4 Pro with 48 GB** or **Mac Studio M1 Max / Ultra** with 64\u2013128 GB offer some of the most realistic value per dollar.  \n- **Expect to pay**: ~$1,700-$2,000 for Minis with 48 GB; similar pricing or slightly more for Studios with similar memory.  \n- **Laptop options** (MacBook Pro maxed): higher premium, especially for battery condition; often double the price punch for similar memory and GPU cores.\n\n---\n\nIf you tell me your budget (e.g. \u201cunder $2,500,\u201d \u201csub-$1,500\u201d) and what size of models you want to run (e.g. \u201c20B-model inference,\u201d \u201c70B feral experiments,\u201d \u201cfine-tuning / long context\u201d) I can pick some specific used Mac models that are realistic matches for you and compare them with non-Mac options. Want me to pull those together?"
    },
    {
        "role": "user",
        "content": "give%20prices%20for%20slightly%20newer%20or%20higher%20end%20refurbs%20like%20mac%20m4%20studio%20or%20m3%20ultra"
    },
    {
        "role": "assistant",
        "content": "Here are some recent examples of **newer/high-end refurbished Apple Mac Studios** (M4 and M3 Ultra), what their configurations are, and what people are paying for them on secondary/resell/refurb channels\u2014in the U.S. market where possible. Prices may vary with condition, warranty, and seller.\n\n---\n\n## Apple Certified Refurbished Direct from Apple\n\nThese are more \u201cofficial\u201d refurb units, with warranty from Apple:\n\n| Model | Key Specs | Refurbished Price* |\n|-------|----------------------------|----------------------|\n| Mac Studio with **M4 Max** (entry/base configurations) | Full stock of ports, likely 36-GB \u201cbase\u201d memory / GPU core setups | **Starts around $1,699** compared to new ~$1,999. ([macrumors.com](https://www.macrumors.com/2025/06/12/refurbished-mac-studio-m4-max-m3-ultra/?utm_source=openai)) |\n| Mac Studio **M3 Ultra** (lowest-end Ultra configuration) | 32-core CPU, 80-core GPU, \u201cbase\u201d Ultra memory / storage allowed | **Starts around $3,399** (new MSRP ~$3,999) on the refurb store. ([macrumors.com](https://www.macrumors.com/2025/06/12/refurbished-mac-studio-m4-max-m3-ultra/?utm_source=openai)) |\n| Higher-spec M3 Ultra (28-core CPU / 60-core GPU) | Likely more RAM / SSD upgrades | **$5,609** refurbished in Apple Store for this configuration. ([apple.com](https://www.apple.com/shop/product/G1CEALL/A?utm_source=openai)) |\n| Super high-end M3 Ultra (256 GB RAM / 8 TB SSD) | Max or near-max spec in memory + storage | **$7,899** as a refurb unit directly from Apple. ([apple.com](https://www.apple.com/shop/product/G1CEHLL/A?utm_source=openai)) |\n\n> \\*Prices are typically ~15% less than new when Apple has stock. ([macrumors.com](https://www.macrumors.com/2025/06/12/refurbished-mac-studio-m4-max-m3-ultra/?utm_source=openai))\n\n---\n\n## eBay / Resale / 3rd-Party Refurb Listings\n\nHere are some recent eBay or non-direct resale examples. Prices fluctuate heavily by RAM/SSD, condition, and warranty, but these give good benchmarks:\n\n| Model / Spec | Price (Approx) | Notes / Condition |\n|--------------|------------------|--------------------|\n| Mac Studio **M4 Max**, 36 GB RAM, 512 GB SSD | ~$1,900 | Recently listed on eBay. Good deal for \u201cmid-spec\u201d M4 Max. ([ebay.com](https://www.ebay.com/itm/277201189453?utm_source=openai)) |\n| Mac Studio **M3 Ultra**, 256 GB RAM / 2 TB SSD (28-core CPU / 60-core GPU) | ~$2,200 | Reddit \u201cused / value-check\u201d post; condition assumed good. ([reddit.com](https://www.reddit.com/r/MacStudio/comments/1ratt9d/realistic_used_price_check_m3_ultra_mac_studio/?utm_source=openai)) |\n| Higher-end, \u201cmonster\u201d M3 Ultra 512 GB RAM / 4 TB SSD | $$ higher; still rare / often overpriced | \u201cValue-check\u201d post mentions listings up much higher; many felt sellers asking several thousand more. ([reddit.com](https://www.reddit.com/r/MacStudio/comments/1rl376m/value_check_mac_studio_m3_ultra_512gb_ram_4tb/?utm_source=openai)) |\n\n---\n\n## What Drives the Price Up or Down\n\nA few factors that cause a lot of the volatility:\n\n- **Memory (Unified RAM)**: 64 GB, 128 GB, 256 GB models fetch significantly more. Ultra / Max chips with high RAM & SSD upgrades will be double or more over base.\n- **SSD Capacity**: Going from 1 TB to 2 TB (+) adds big dollars in resale.\n- **Condition / Warranty**: Apple certified or third-party refurb with warranty holds up value much better.\n- **Scarce Configs / RAM Supply**: Because Apple has paused or removed some high RAM tiers (e.g. 512 GB Ultra) from direct purchase due to supply constraints, rarer high-RAM models demand premium. ([tomshardware.com](https://www.tomshardware.com/desktops/apple-quietly-axes-128gb-mac-studio-amid-supply-constraints-and-local-ai-frenzy-highest-memory-capacity-reduced-to-96gb-two-months-after-discontinuation-of-512gb-model?utm_source=openai))\n\n---\n\nIf you tell me your desired RAM/SSD combo **and** your target budget, I can list a few current live listings that match close to your sweet spot\u2014good, clean deals."
    }
]