• ugjka@lemmy.ugjka.net
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    2 days 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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      2 days 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.

      • Rimu@piefed.social
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        2 days 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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          2 days 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.

          • boonhet@sopuli.xyz
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            1 day ago

            Yup, you want memory accessible to the GPU for local AI. AMD Strix Point and Mac devices are popular options. CPU can run LLMs but very slowly. I’ve got 32 GB of RAM and 8 VRAM and it’s borderline useless for models that don’t fit in the VRAM.

          • SabinStargem@lemmy.today
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            1 day ago

            You can use something like KoboldCPP on Linux, which allows both RAM and VRAM combined to run a model. O’course, not as fast when compared to pure VRAM or the Mac approach, but it is an option. I use my 128gb RAM with some GPUs for running models.

              • SabinStargem@lemmy.today
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                20 hours ago

                Speed depends on how much of the model is on VRAM, and the dense/MoE architecture of that model. The RAM’s benefit is more about having the ability to run the model in the first place. In any case, a dense Qwen3.6 27b would take up about 27-33gb-ish of memory, plus whatever context size you set.

                Upcoming implementation of MTP will increase the size of models, but in exchange, they will also run faster. About a 30%ish boost for dense models, a bit less for Mixture of Expert varieties, from the looks of it.

                • boonhet@sopuli.xyz
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                  20 hours ago

                  When I’ve tried running a ~14 gigabyte distillation of whatever model it is I was trying to run, it would come out super slow at I believe 50/50 GPU to CPU. It gets so slow it was just more bearable to run a 7 or 8 b model that would actually fit entirely in VRAM and run entirely on GPU. Also made the rest of computer usage more bearable.

                  To be fair I do only have a 6 core 6 thread CPU though. It shot up to 600% usage so even the DDR4 memory wasn’t really bottlenecking it. I suspect a 9950X would fare a lot better.

                  • SabinStargem@lemmy.today
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                    18 hours ago

                    I am using a 5950x, with 128gb of DDR4 3600 memory. The GPUs are a 3060 and 4090, totaling 36gb of VRAM. IMO, being bottlenecked by the CPU is definitely a thing, it just comes third after the VRAM and RAM considerations.

                    With a 35b+3a MoE at Q8 with KV8, I get…

                    [11:54:32] CtxLimit:18858/262144, Init:0.18s, Processed:17294 in 7.66s (2259.18T/s), Generated:1564/32768 in 29.01s (53.91T/s), Total:36.85s

      • humanspiral@lemmy.ca
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        1 day 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

        • chilicheeselies@lemmy.world
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          1 day ago

          Depends on if you even need a better model though. Can you run a good enough model is what matters for the most part.