<div dir="ltr"><div class="gmail_default" style="font-size:large">I've been knee deep in local llm setup for the better part of the last two weeks. To give you perspective on my hardware, I'm playing with two moderately beefy "consumer" machines: a Windows 11-based Ultra 7, 64GB RAM, RTX 4070 w/ 12GB VRAM and a linux-based Ryzen AI 9 HX370 mini pc with 64GB RAM (both bought before prices went bonkers thankfully).</div><div class="gmail_default" style="font-size:large"><br></div><div class="gmail_default" style="font-size:large">TLDR: I'm keeping my Claude and Gemini subscriptions.</div><div class="gmail_default" style="font-size:large"><br></div><div class="gmail_default" style="font-size:large">I think a longer discussion will hinge on what you want to do with it. Are you programming? Running OpenClaw/agentic stuff? Just chatting? Creating documents and presentations? Doing NotebookLM kind of things? Because here's the deal. After a LOT of tweaking, I'm getting:</div><div class="gmail_default" style="font-size:large"><ul><li>around 25 tokens per second output on my iGPU (Ryzen) using some pretty high quality models (Qwen 3.6 35b and Gemma 4 26b) by pushing the VRAM up to 48GB.</li><li>anywhere between 65 and 95 tokens per second output on my RTX GPU using much lower quality models (Qwen 3.5 9b and Gemma 4 4b).</li></ul><div>In both cases, if I don't take steps to optimize the model such that it stays 100% in VRAM, it slows to an entirely unusable rate.</div><div><br></div><div>UP FRONT WARNING--I'm a noob with this, so take my explanation with a grain of salt.</div><div><br></div><div>What does that actually mean? Well especially if you use a "reasoning" model like Qwen, then a simple query response (like "tell me a funny dad joke") can take up to a minute to respond. This is because approximately every word of every "thought" is an output token. It "talks to itself" until if decides it has found a reasonable answer.
And it adds up quick.
Here are some basic results for this exact query on all 4 models / hardware:</div><div><ul><li>Qwen on RTX 4070: required 1200 tokens and 18 seconds to respond</li><li>Gemma on RTX 4070: required 300 tokens and 3.5 seconds to respond</li><li>Qwen on iGPU: required 630 tokens and 23 seconds to respond</li><li>Gemma on iGPU: required 450 tokens and took 18 seconds to respond</li></ul><div>Again that's moderately beefy hardware and a lot of tweaking. But I could also do far better if I accepted much dumber models. Which may be just find for basic agentic work. But much less good for coding or reasoned synthesis. And the "smartest" model took 23 seconds to reason through a dad joke. 7 seconds just to figure out how to respond to "hello". Working on truly complex reasoning can take a bathroom+coffee break to give you back results.</div></div><div><br></div><div>Note too that this doesn't take into account context size and context caching. Context is the LLM's active memory. Models have maximums (I think they are tuned for these sizes). But in most cases you'll likely have to accept something lower. However, to be even moderately useful for much of anything, you can't go terribly low. Coding tools and agentic tools just blow up if they can't remember things from one thought to the next.</div><div><br></div><div>The numbers I'm getting on the RTX are decent. Almost usable. BUT the context sizes to achieve that make it basically unusable for the kind of work I want to do. If I bump up the context window to a usable level with these models, I leak out into RAM (far slower than VRAM) and my performance tanks to unusable levels (like < 5-10 tps...at least 80-90% or more slower).</div><div><br></div><div>The iGPU with huge VRAM is slower than the dedicated GPU, but because I can crank up the context window, they actually become usable for what I want to use them for. However...speed. Claude Sonnet or Gemini Flash are easily 10x faster at everything. And more like 20x-30x faster for most reasoning work. So at some point it's a question of how much you value your time.</div><div><br></div><div>I will be using local models for some basic stuff, I think. I'm starting down a personal knowledge management path with AnythingLLM or something similar. I think it'll pair perfectly with this. And I'll likely find more ways to leverage it. But I won't be abandoning the big boys any time soon.</div><div><br></div><div>And sadly, although your Ultra 7 w/ 32GB RAM is an awesome PC, I fear your experience with local llms for anything other than experimentation and learning will prove frustratingly slow. And with prices the way they are right now, getting your PC spec'd to perform moderately well will certainly cost around the same as a full year of one of the "ultimate" plans.</div><div><br></div><div>Hope this was helpful. And that I didn't show my ignorance too badly.</div><div><br></div><div>Best,</div><div>Paul York</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, May 20, 2026 at 11:35\u202fPM Lewis Wood via NFBCS <<a href="mailto:nfbcs@nfbnet.org" target="_blank">nfbcs@nfbnet.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div><div lang="EN-US"><div><p class="MsoNormal"><span style="font-size:11pt">I am currently learning as well.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">I am now doing Ollama playlist lessons #2 currently.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><a href="https://www.youtube.com/playlist?list=PLvsHpqLkpw0fIT-WbjY-xBRxTftjwiTLB" target="_blank">https://www.youtube.com/playlist?list=PLvsHpqLkpw0fIT-WbjY-xBRxTftjwiTLB</a><u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">I did my initial research on Lm Studio before I learned about Ollama CLI<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">This was my first Lm Studio and it was an excellent one regarding resources, models, agents, etc. Even discussed how to load partial in differing areas gpu and ddr.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><a href="https://www.youtube.com/watch?v=UngVdAsQEiU" target="_blank">https://www.youtube.com/watch?v=UngVdAsQEiU</a><u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">You can search youtube \u201clm studio\u201d<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">Lewis Wood<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><div><div style="border-width:1pt medium medium;border-style:solid none none;border-color:rgb(225,225,225) currentcolor currentcolor;padding:3pt 0in 0in"><p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif"> NFBCS <<a href="mailto:nfbcs-bounces@nfbnet.org" target="_blank">nfbcs-bounces@nfbnet.org</a>> <b>On Behalf Of </b>Joe Orozco via NFBCS<br><b>Sent:</b> Wednesday, May 20, 2026 10:08 PM<br><b>To:</b> 'NFB in Computer Science Mailing List' <<a href="mailto:nfbcs@nfbnet.org" target="_blank">nfbcs@nfbnet.org</a>><br><b>Cc:</b> Joe Orozco <<a href="mailto:jsorozco@gmail.com" target="_blank">jsorozco@gmail.com</a>><br><b>Subject:</b> [NFBCS] Local AI<u></u><u></u></span></p></div></div><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal"><span style="font-size:11pt">Hello,<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">With Google following in Claude\u2019s footsteps in terms of usage restrictions, can anyone speak to their experience using local LLM options? I\u2019m looking at Jemma 4 and trying to understand how accessible this route might be with JAWS on Windows.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">I\u2019m on a fairly decent machine: 32 GB RAM, Ultra 7 processor, 4 TB SSD. I see they\u2019re recommending GPU for some of the more robust models, but I want to think most of what I\u2019m doing shouldn\u2019t require gaming machine specs. If you beg to differ though, let me know.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">If anyone can speak to Jemma alternatives, I\u2019d also be interested. I don\u2019t think I\u2019ll suspend my subscriptions, but with these usage limitations feeling like the new standard, I want to spread my usage a little so that I don\u2019t feel like I need to be hitting the top subscriptions just to get more mileage out of the five-hour increments.<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">Thanks in advance for any tips,<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11pt">Joe<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-family:"Times New Roman",serif">--<u></u><u></u></span></p><p class="MsoNormal"><span style="font-family:"Times New Roman",serif">Joe Orozco: Your Message, My Mission<u></u><u></u></span></p><p class="MsoNormal"><span style="font-family:"Times New Roman",serif"><a href="https://joeorozco.com/services/" target="_blank">https://joeorozco.com/services/</a><u></u><u></u></span></p><p class="MsoNormal"><u></u> <u></u></p></div></div>_______________________________________________<br>
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