Some ppl don't like to hear it. But I would assume that token costs when using an inference provider are cheaper than electricity of using locally.
If we just take into account output token generation for simplicity. With 5tps u get 180k tokens an hour. That would costs around 0.05USD from an inference provider.
I estimate that the server consumes probably around 500W during inference.
In Germany where 1kwh cost around 0.3USD, 180k tokens inferred locally would therefore cost 0.15USD which is 3x the costs of using an inference provider.
But for ppl who worry about their data, running locally might still be good. However, they should be aware, that it is much less efficient than using an inference provider.
The efficiency gap will also significantly increase as new GPUs will make inference much more efficient.
But you said 7-9 tokens/second, that's not a decent speed. I'm not an expert by all means but in my local experiments, less than 12 to 16 tps is too slow.
You think of an idea that you want to have the LLM process, queue it up, and go back to what you were doing. Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete. It's kind of like using a 3D printer: It doesn't matter if a print takes 10 hours, because when you come back in the morning it will be done.
Yes, with top-tier GPU farms you can hit hundreds of tokens per second. But if the old Xeon in the closet can get useful work done at 5 tokens per second, there are lots of people and lots of use cases where a free, unlimited 5 TPS stream is worth more than paying a dollars per day to get access to a 500 TPS source.
For me, at least for agentic use, you need at least ~40tps. Less might be good only for tasks you could run in the background (like at night maybe).
Instead of coding agents like tool for local models I would like to see more "docker ps" like tools where you can queue up tasks that get processes incrementally (maybe when the pc i idle) and are specialized for doing retries and caching as much work as possible to work decently with slow local models.
Yep, and we don't even know how long they spent on prefill. A typical 50-100k token session could take 10-20 minutes to prefill on a Mac.
Pending any new hardware or radically different LLM architectures, we're going to be waiting a lot longer than 2027 for 200B models on local hardware. SOC platforms like Apple Silicon are hamstrung by their obsession over raster performance, the hardware lacks the fundamental GPGPU hardware to be a replacement for real-world inference.
>>It's quite telling you didn't use Qwen3.6-35B-A3B locally to build that
that would have run into a race condition unfortunately ;)
but there is a sample landing page + a python function on the repo which shows what the model produced. my goal is to integrate the local model in my workflow so that claude/OAI can call this model for basic stuff.
Have you tried with a single CPU to get rid of the NUMA penalty? I understand this likely means halving the memory but I am interested in how much of a difference it makes
I have (192GB machine with two CPUs), pretty much does the trick. It just runs some small models used for embedding, etc. and has those on one CPU / memory node and all the Docker containers on the other one.c
I have a dual xeon also, same as OP: Ivy Bridge + 128GB DRAM, and was never really able to get decent LLM performance out of it. So I ended up biting the bullet and adding a "budget tier" A4000 20GB GPU. Too bad all my DRAM is wasted now--not sure if there is a way to take advantage of lots of DRAM once you move over to having inference happening on the GPU.
I run the same setup Gemma 4 26B on a 2013 Mac Pro (dual graphics cards but they're useless for this). I also get about 5 t/s. It's perfectly serviceable for some tasks!
Is it just me or does this post not mention how much RAM they had? I would love to know - I have a dual-Xeon 1U screamer with 96GB of DDR4 RDIMM just sitting around...
FWIW I'm getting a hardware max of 20 tok/s (approx topping out the GPU's compute) on my custom local diffusiongemma port running on an M3.
To me context means everything.
Tokens per second is a great metric but in the real world context window is the deal breaker when a real use case is on the table.
Author here. The short version: a viral post ran Gemma 4 on a 2016 Xeon; my Xeons are 2013, and the fork it used assumes AVX2, which Ivy Bridge doesn't have. The build failure was easy. The fun bug was the silent one: two MoE graph ops with no dispatch case on non-AVX2 builds, so every expert FFN output was uninitialized memory. Deterministic, NaN-free, fluent-looking multilingual gibberish.
The fix is open upstream as PR #2138 (https://github.com/ikawrakow/ik_llama.cpp/pull/2138), awaiting review. Fair warning on the AI angle: the patch was written by Claude at my direction. The post is explicit about which parts were me and which weren't. Happy to answer questions about either the bug or the workflow.
Here's the thing: life also imitates art. If you invert your load-bearing assumption, it could be that he just reads too much slop. But my honest take? You might be right.
If we just take into account output token generation for simplicity. With 5tps u get 180k tokens an hour. That would costs around 0.05USD from an inference provider.
I estimate that the server consumes probably around 500W during inference.
In Germany where 1kwh cost around 0.3USD, 180k tokens inferred locally would therefore cost 0.15USD which is 3x the costs of using an inference provider.
But for ppl who worry about their data, running locally might still be good. However, they should be aware, that it is much less efficient than using an inference provider.
The efficiency gap will also significantly increase as new GPUs will make inference much more efficient.
I am running Qwen3.6-35B-A3B locally on my 16GB mac with 7-9 tokens/second. Link - https://github.com/deepanwadhwa/samosa-chat
This is a GPT4 level model running locally with a decent speed on a 16gb ram macbook air.
But you said 7-9 tokens/second, that's not a decent speed. I'm not an expert by all means but in my local experiments, less than 12 to 16 tps is too slow.
https://mikeveerman.github.io/tokenspeed/?rate=10&mode=text
You think of an idea that you want to have the LLM process, queue it up, and go back to what you were doing. Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete. It's kind of like using a 3D printer: It doesn't matter if a print takes 10 hours, because when you come back in the morning it will be done.
Yes, with top-tier GPU farms you can hit hundreds of tokens per second. But if the old Xeon in the closet can get useful work done at 5 tokens per second, there are lots of people and lots of use cases where a free, unlimited 5 TPS stream is worth more than paying a dollars per day to get access to a 500 TPS source.
Instead of coding agents like tool for local models I would like to see more "docker ps" like tools where you can queue up tasks that get processes incrementally (maybe when the pc i idle) and are specialized for doing retries and caching as much work as possible to work decently with slow local models.
Pending any new hardware or radically different LLM architectures, we're going to be waiting a lot longer than 2027 for 200B models on local hardware. SOC platforms like Apple Silicon are hamstrung by their obsession over raster performance, the hardware lacks the fundamental GPGPU hardware to be a replacement for real-world inference.
It's quite telling you didn't use Qwen3.6-35B-A3B locally to build that, seems there was another collaborator ;)
Show something you've built with the model+tooling instead, truly dogfood it. I'm sure you'll discover things along the way too!
that would have run into a race condition unfortunately ;)
but there is a sample landing page + a python function on the repo which shows what the model produced. my goal is to integrate the local model in my workflow so that claude/OAI can call this model for basic stuff.
That is no where near decent at all.
https://gist.github.com/hparadiz/f3596d00a62d8ebb2dadcc46ee5...
https://news.ycombinator.com/item?id=48354801
FWIW I'm getting a hardware max of 20 tok/s (approx topping out the GPU's compute) on my custom local diffusiongemma port running on an M3.
Whether the writer's setup affects that choice I don't know.
A 10 year old Xeon is all you need
https://news.ycombinator.com/item?id=48353348
The fix is open upstream as PR #2138 (https://github.com/ikawrakow/ik_llama.cpp/pull/2138), awaiting review. Fair warning on the AI angle: the patch was written by Claude at my direction. The post is explicit about which parts were me and which weren't. Happy to answer questions about either the bug or the workflow.
I don't think the post itself reads like AI at all, but that's just me.
Android studio connected to a local model disconnects automatiacally after 10 minutes. How set this limit to 12 hours or remove it completely?
I could run my LM studio model all night... but I cant, since Android studio times out after a hard limit of 10M.
This is not related to number of tokens. I do 130 sec.