• ugjka@lemmy.ugjka.net
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    13 hours ago

    Self hosting an llm ain’t the same thing as self hosting nextcloud for your docs and calendar. Yes there are small models but their output is laughable

    • MagicShel@lemmy.zip
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      11 hours ago

      Small models are improving and becoming more capable. The quality of local LLMs is basically unbounded. The context size of local LLMs is bounded by hardware. So local LLMs can be very capable for small, self-contained tasks.

      qwen 3.6 35b running locally:

      Write a Python script that can pull weather data from public sources and provide the high and low temperature for the current day in Miami, FL.
      

      Single shot. No tool/internet use, so it didn’t pull this script from elsewhere.

      import requests
      
      def get_miami_weather():
          # Miami, FL coordinates
          LATITUDE = 25.7617
          LONGITUDE = -80.1918
      
          # Open-Meteo API URL (free, no API key required)
          url = (
              f"https://api.open-meteo.com/v1/forecast?"
              f"latitude={LATITUDE}&longitude={LONGITUDE}"
              f"&daily=temperature_2m_max,temperature_2m_min"
              f"&timezone=auto"
          )
      
          try:
              response = requests.get(url, timeout=10)
              response.raise_for_status()  # Raises error for 4xx/5xx HTTP status codes
              data = response.json()
      
              # Index 0 corresponds to the current day
              high_c = data["daily"]["temperature_2m_max"][0]
              low_c = data["daily"]["temperature_2m_min"][0]
      
              # Convert to Fahrenheit (commonly used in the US)
              high_f = (high_c * 9/5) + 32
              low_f = (low_c * 9/5) + 32
      
              print("🌤️  Miami, FL Weather for Today:")
              print(f"High: {high_f:.1f}°F ({high_c:.1f}°C)")
              print(f"Low:  {low_f:.1f}°F ({low_c:.1f}°C)")
      
          except requests.exceptions.HTTPError as http_err:
              print(f"❌ HTTP error occurred: {http_err}")
          except requests.exceptions.ConnectionError:
              print("❌ Error: Could not connect to the weather API.")
          except requests.exceptions.Timeout:
              print("❌ Error: Request timed out.")
          except requests.exceptions.RequestException as err:
              print(f"❌ An error occurred: {err}")
          except KeyError as key_err:
              print(f"❌ Error parsing data: Missing expected key {key_err}")
          except Exception as err:
              print(f"❌ Unexpected error: {err}")
      
      if __name__ == "__main__":
          get_miami_weather()
      

      Output:

      % python3 ./m_weather.py
      🌤️  Miami, FL Weather for Today:
      High: 88.0°F (31.1°C)
      Low:  73.2°F (22.9°C)
      

      I tried to keep the size and scope within something that would reasonably fit in a comment. Looks pretty decent to me, but I can’t write Python myself. Never learned. I double-checked the LAT & LON of Miami, and it’s spot on.

      It did take 47 seconds, while a cloud LLM would probably take 5 or less.

      All I’m saying is local LLM isn’t garbage and it is getting better all the time.

      • humanspiral@lemmy.ca
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        4 hours ago

        qwen 3.6 is awesome, but 48-64gb is still real money these days. (though 32gb on dedicated separate machine is also more money). Sonnet 3.5 to opus 4.5 level benchmarks. and the online cost metrics for 27b and 35b are way off considering the overall usefulness of a 48-64gb machine (inclusive of gpu vram for 35b) which even in single, non batching, use could displace $5-$7/day of use.

        Local costs are much lower than online costs in linked chart, but if online, there are better models

      • Rimu@piefed.social
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        10 hours ago

        That’s interesting.

        How much ram did it use while running?

        If you used a GPU, how much does it cost in today’s prices?

        • MagicShel@lemmy.zip
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          6 hours ago

          It’s a MacBook Pro. 36GB of ram. I am sure Macs have some kind of gpu and I understand it somehow combines GPU ram with system ram, but I don’t really know Mac hardware very well.

          It’s beefy for a laptop, but the desktop I built for myself several years ago had 32 GB of ram and a GTX 1660, so I’m guessing they are similar in capability. I gave that to my daughter, so I can’t run a comparison right now.

          EDIT: After doing just a bit of research, I’ve learned the unified memory architecture that Macs use, while not ideal for many purposes, is actually a big advantage for running larger inference models. So it’s possible that this particular model wouldn’t run at all on my Linux box or would run much slower because the full model wouldn’t fit in the 6GB of VRAM and create a lot of memory thrashing.