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a guy put a local AI coding stack on an Android phone Termux on the screen Ollama underneath a local model answering from the device that tiny terminal is the shift. two years ago, local LLMs felt like punishment fans screaming bad models one token per second cloud looked...

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Myttle

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27,108 просмотров • 1 месяц назад •via X (Twitter)

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You can run Qwen 3.5 + OpenClaw + Ollama for free. No subscriptions, just a working local agent stack. Do this. → Install Ollama → Pull Qwen 3.5 Cloud inside Ollama → Point your tool, like Claude Code or OpenClaw, at the Ollama endpoint → Stay within the weekly token allowance → Ship one real output, like a lead magnet or landing page Save this video, you’ll build a free AI stack that actually does work. Want the SOP? DM me. 💬
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You can run Qwen 3.5 + OpenClaw + Ollama for free. No subscriptions, just a working local agent stack. Do this. → Install Ollama → Pull Qwen 3.5 Cloud inside Ollama → Point your tool, like Claude Code or OpenClaw, at the Ollama endpoint → Stay within the weekly token allowance → Ship one real output, like a lead magnet or landing page Save this video, you’ll build a free AI stack that actually does work. Want the SOP? DM me. 💬

Julian Goldie SEO

30,195 просмотров • 4 месяцев назад

THEY RAN A LOCAL AI MODEL ON A RASPBERRY PI THAT COSTS LESS THAN $300 no cloud no subscription no data center device smaller than your wallet output on a 27 year old iMac I tested local ai this week on a macbook and a phone the hardware keeps getting cheaper and the models keep getting smarter bookmark this if you’re still paying $20 a month for something your $300 raspberry pi can do
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THEY RAN A LOCAL AI MODEL ON A RASPBERRY PI THAT COSTS LESS THAN $300 no cloud no subscription no data center device smaller than your wallet output on a 27 year old iMac I tested local ai this week on a macbook and a phone the hardware keeps getting cheaper and the models keep getting smarter bookmark this if you’re still paying $20 a month for something your $300 raspberry pi can do

leopardracer

73,534 просмотров • 1 месяц назад

local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.
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local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.

sabesh 📟

31,497 просмотров • 1 месяц назад

A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.
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A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.

Nekt0

54,212 просмотров • 11 дней назад

Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.
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Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.

BridgeMind

62,800 просмотров • 1 месяц назад

Claude Desktop can now run with free local models. This is a huge workflow shift. → Install Ollama. → Run local models on your machine. → Connect Ollama to Claude Code. → Launch Claude Desktop with Ollama. → Swap models depending on the task. Save this video, you’ll use Claude with way more freedom. Want the SOP? DM me. 💬
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Claude Desktop can now run with free local models. This is a huge workflow shift. → Install Ollama. → Run local models on your machine. → Connect Ollama to Claude Code. → Launch Claude Desktop with Ollama. → Swap models depending on the task. Save this video, you’ll use Claude with way more freedom. Want the SOP? DM me. 💬

Julian Goldie SEO

11,444 просмотров • 2 месяцев назад

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid
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A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

Gipp 🦅

456,647 просмотров • 1 месяц назад

HE STOPPED RENTING AI AND BOLTED FOUR USED RTX 3090s TO ONE BOARD watch the table: four five-year-old GPUs, 24GB of VRAM each. 96GB of local model memory sitting under one operator’s control. the build looks extreme. the subscription math is worse. then opened his statement and found $340/month going to seven AI companies. six cancellations saved $140 immediately. then he realized a single used 3090 for roughly $700 could already run Qwen 27B locally: - no token meter - no rate limits - no files leaving the machine - roughly 25–30 tokens per second you do not need the four-card monster in this clip. one card moves most daily coding, writing and document work off the cloud. the old flex was paying for every frontier model. the new flex is owning the compute they keep renting back to you.
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HE STOPPED RENTING AI AND BOLTED FOUR USED RTX 3090s TO ONE BOARD watch the table: four five-year-old GPUs, 24GB of VRAM each. 96GB of local model memory sitting under one operator’s control. the build looks extreme. the subscription math is worse. then opened his statement and found $340/month going to seven AI companies. six cancellations saved $140 immediately. then he realized a single used 3090 for roughly $700 could already run Qwen 27B locally: - no token meter - no rate limits - no files leaving the machine - roughly 25–30 tokens per second you do not need the four-card monster in this clip. one card moves most daily coding, writing and document work off the cloud. the old flex was paying for every frontier model. the new flex is owning the compute they keep renting back to you.

Myttle

73,923 просмотров • 11 дней назад

NVIDIA MADE A $249 DESK CHIP THAT TURNS LOCAL LLMS INTO A TINY PRIVATE BACKEND jetson orin nano super is basically a pocket-size ai board: 67 TOPS, 1024-core ampere gpu, 8gb memory, 7-25w power draw. not a gaming pc, not a rented cloud box, just a small nvidia dev kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.
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NVIDIA MADE A $249 DESK CHIP THAT TURNS LOCAL LLMS INTO A TINY PRIVATE BACKEND jetson orin nano super is basically a pocket-size ai board: 67 TOPS, 1024-core ampere gpu, 8gb memory, 7-25w power draw. not a gaming pc, not a rented cloud box, just a small nvidia dev kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.

Gipp 🦅

39,609 просмотров • 1 месяц назад

AI that knows the room: stays silent, speaks on cue, and routes to the right model (local or cloud). No wake words. No cameras. On-device. Demo ↓ 🔊
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AI that knows the room: stays silent, speaks on cue, and routes to the right model (local or cloud). No wake words. No cameras. On-device. Demo ↓ 🔊

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33,943 просмотров • 9 месяцев назад

Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.
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Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.

Perplexity

350,655 просмотров • 1 месяц назад

Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.
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Stop paying for Claude Code. You can run it for FREE. No API tokens. No monthly plan. No cloud costs. Here’s the simple setup: 1. Download Ollama (free). 2. Install a local model like GLM 4.7 Flash. 3. Launch Claude Code using that local model. 4. Done. You’re now running Claude Code fully powered by a local model. Same interface. No API bill. You can even run OpenClaw the same way. Local AI agents. Free. 24/7. Most people are overpaying because they don’t know this exists.

Julian Goldie SEO

103,710 просмотров • 4 месяцев назад

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
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HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 просмотров • 2 месяцев назад

THE NEXT WAVE OF LOCAL AI BUILDERS SHIPS PORTABLE STACKS WITH HANDHELD $40 SDR SCANNERS, LOCAL LLMs ONLY WORK OFF-GRID WHEN SOMETHING ELSE IS LISTENING TO THE AIR 00:30 he opens the case and points at each component, "really it's a pi sugar power pack mounted to a raspberry pi zero 2w and a little adafruit bonnet" the scanner adds a noelec sdr dongle and a 2m antenna, boots up as a spectrum analyzer he takes to the park with his kid to identify local radio transmissions the same maker keeps a $600 mini pc running qwen 3.6 27b on his desk with claude code pointed at localhost, the offline ai handles his coding, writing and research without a single cloud api call the sdr scanner completes the sovereignty stack, the mini pc thinks locally while the scanner listens locally, and neither needs the internet for the parts of his work that used to require it builders who moved off cloud ai are now moving off cloud everything, the pattern isn't just $459 in subscriptions dead but a full stack that survives when the cloud goes down bookmark this and read the article below
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THE NEXT WAVE OF LOCAL AI BUILDERS SHIPS PORTABLE STACKS WITH HANDHELD $40 SDR SCANNERS, LOCAL LLMs ONLY WORK OFF-GRID WHEN SOMETHING ELSE IS LISTENING TO THE AIR 00:30 he opens the case and points at each component, "really it's a pi sugar power pack mounted to a raspberry pi zero 2w and a little adafruit bonnet" the scanner adds a noelec sdr dongle and a 2m antenna, boots up as a spectrum analyzer he takes to the park with his kid to identify local radio transmissions the same maker keeps a $600 mini pc running qwen 3.6 27b on his desk with claude code pointed at localhost, the offline ai handles his coding, writing and research without a single cloud api call the sdr scanner completes the sovereignty stack, the mini pc thinks locally while the scanner listens locally, and neither needs the internet for the parts of his work that used to require it builders who moved off cloud ai are now moving off cloud everything, the pattern isn't just $459 in subscriptions dead but a full stack that survives when the cloud goes down bookmark this and read the article below

starmex

40,403 просмотров • 5 дней назад

OSINT + AI 🧠 Vyntral's God’s Eye integrates local AI via Ollama for vuln analysis and reporting. No cloud. No APIs. Fully private. Free. AI is becoming a default layer in open source tooling, not a premium feature. Try it here:
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OSINT + AI 🧠 Vyntral's God’s Eye integrates local AI via Ollama for vuln analysis and reporting. No cloud. No APIs. Fully private. Free. AI is becoming a default layer in open source tooling, not a premium feature. Try it here:

The OSINT Newsletter

53,194 просмотров • 5 месяцев назад

NVIDIA JUST DROPPED NEMO CLAW TO MAKE OPENCLAW SAFE TO RUN ON YOUR OWN MACHINE. It keeps private files off the cloud, runs fully offline, and adds the security layer local AI agents were missing.
6:42

NVIDIA JUST DROPPED NEMO CLAW TO MAKE OPENCLAW SAFE TO RUN ON YOUR OWN MACHINE. It keeps private files off the cloud, runs fully offline, and adds the security layer local AI agents were missing.

0xMarioNawfal

220,034 просмотров • 3 месяцев назад

🚨 do you understand what this AMD engineer just proved on camera watch the clip, it’s all there in one frame: llama.cpp slot logs on the left, nvitop showing 79% GPU util holding steady, localhost chat answering a 26k token prompt at 711 tok/s prompt eval, the gemma-4-12B Q4_K_XL gguf running clean. no cloud call, no api key, no censorship layer. for 2 years the rule was: frontier models need the cloud, local is too slow for real work, pay $200/month and wait for tokens. today a $1,800 lunch box runs qwen 3 235B at 11 tok/s with zero data leaving the room, and the same box runs gemma 4 12B with multi token prediction at 14+ tok/s on 30k context with no throttling. $440/month subscription stack: dead. $200/month claude tier: dead. local is too slow excuse: dead. one lunch box. $9/month in electricity. two moats buried in one week. the you need the cloud line wasn’t protecting users it was protecting the subscription.
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🚨 do you understand what this AMD engineer just proved on camera watch the clip, it’s all there in one frame: llama.cpp slot logs on the left, nvitop showing 79% GPU util holding steady, localhost chat answering a 26k token prompt at 711 tok/s prompt eval, the gemma-4-12B Q4_K_XL gguf running clean. no cloud call, no api key, no censorship layer. for 2 years the rule was: frontier models need the cloud, local is too slow for real work, pay $200/month and wait for tokens. today a $1,800 lunch box runs qwen 3 235B at 11 tok/s with zero data leaving the room, and the same box runs gemma 4 12B with multi token prediction at 14+ tok/s on 30k context with no throttling. $440/month subscription stack: dead. $200/month claude tier: dead. local is too slow excuse: dead. one lunch box. $9/month in electricity. two moats buried in one week. the you need the cloud line wasn’t protecting users it was protecting the subscription.

AdiiX

22,163 просмотров • 23 дней назад

The engine of the 21st century is here. 🧠 The QVAC SDK is the "steam engine" of the AI era—decoupling intelligence from the cloud and putting it in your hands. A single API for local-first, modular AI that runs anywhere. - Sovereign: Own your engine, don't rent it. - Local: 0 latency, no cloud dependency. - Modular: Stackable, universal building blocks. The era of Stable Intelligence has begun.
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The engine of the 21st century is here. 🧠 The QVAC SDK is the "steam engine" of the AI era—decoupling intelligence from the cloud and putting it in your hands. A single API for local-first, modular AI that runs anywhere. - Sovereign: Own your engine, don't rent it. - Local: 0 latency, no cloud dependency. - Modular: Stackable, universal building blocks. The era of Stable Intelligence has begun.

QVAC

10,663,355 просмотров • 3 месяцев назад

Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:
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Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

Daniel San

65,125 просмотров • 1 год назад

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth
0:40

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Marktechpost AI Dev News ⚡

31,551 просмотров • 6 месяцев назад