> Write a compact Python function that parses a unified diff and returns the changed file paths. Then explain two edge cases.
> Each benchmark generated about 128 tokens.
Generating 128 tokens is probably not enough for good benchmark results. MTP speedup depends on how often the predicted tokens are accepted. In my experience, the very early output has a higher acceptance rate, so short testing can give false positive speedups.
llama.cpp includes a tool specifically for benchmarking that will sweep the arguments for you so you don't have to restart the server and send it prompts:
https://github.com/ggml-org/llama.cpp/blob/master/tools/llam...
EDIT: Also the section about downloading the models should have mentioned that llama.cpp has a "-hf" argument that will download the models for you. I appreciate the author for sharing their experience, but for beginners this might not be the best guide to use.
Yeah, I didn't write this as a proper developer guide. My screen recording started getting loads of favourites and I started getting messages asking about how I set it up, so just through up a quick rundown of how I setup this test.
I little just saw the Unclothe announcement about "Double the speed" and thought "Ha. I wonder if that will get it fast enough I'd actually be prepared to use it" and had a go at setting it up.
I'd done tests before last year with things like Devstral, but they were always both so slow and dumb, I didn't want to bother.
This finally hit the "wow, this is useable" level of both speed and intelligence.
Are you sure you did not mean Unsloth?
llama.cpp includes tools for that, what you are looking at is to have a prefill before token generation to measure it properly. Increasingly also, measuring token generation speed at longer context (32k or 64k) is important too.
As for oprncode, doesn't the system prompt eat too much of the context? Local models are really constraint in regards contex, and opencode AFAIR uses a 10k of it or some thing close.
LLAMA_CACHE="models" ./llama-server \
-hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL \
...-hfd for the draft model.
Not knocking huggingface-cli, just find it's much easier for people to try out this stuff when they can just
mise use --global github:ggml-org/llama.cpp
LLAMA_CACHE="models" llama-server \
-hf unsloth/gemma-4-26B-A4B-it-qat-GGUF:UD-Q4_K_XL \
--host 0.0.0.0 \
--port 11434 \
... —no-mmproj
You would not need to follow a blog post with omlx IMHO
sbx policy set-default open
The Gemma 4 MLX builds I have found so far have been slower at the same quantisation and much slower with MTP.
The built-in web UI for llama.cpp is really quite good once you have chosen your model. Otherwise I quite like LM Studio for tinkering.
One thing I would say is that both Gemma-4 and Qwen 3.6 simply do not need a large chunk of the typical opencode system prompt. Better off without it.
I have not done in-depth, really controlled testing and there is much about performance tuning I don't understand, but it's fairly clear to me that on an M1 Max, MLX does not have the massive advantage it may have on other machines or other models.
It is wholly possible that MLX is _much_ better on the M3 and up, because the neural engine is that much better.
Frankly I think llama.cpp may simply have caught up quite a lot.
MTP is the same issue. There is always a chance that adding a separate MTP draft model has more compute overhead than it brings in terms of speedup, and since I am using an older machine and the MoE models, I am not actually in a zone where MTP can actually add much. What happens is that there's an enormous advantage in speed handling while the prompt and the early reasoning and it then tails off dramatically to be worse, on average, than non MTP.
(Qwen 3.5 35B shows, possibly, a small advantage if its internal MTP is enabled. But it is small — 10% maybe.)
For the 26B Gemma 4, MLX and MTP combined were noticeably slower than the GGUF is with llama.cpp.
If it were a newer machine with a larger, dense model, I'd definitely expect to see an advantage from MTP, and it is possible that there are some parameters I can tweak (duplicate token penalty, temperature, shared cache stuff) that give MTP more of an edge (keep its successful prediction rate higher).
Either way, it feels like the smallish gain I will see on this particular bit of kit might not be worth the long, long journey down that rabbit hole right now.
I'm using the GGUF too; it appears slightly faster in llama.cpp now than current LM Studio but it's not clear to me if that is down to LM Studio having a little more code overhead, older llama.cpp under the hood, or just parameter differences.
https://github.com/ljubomirj/ds4/tree/reap-compact-support
DS4F dropping to unusable <10 tok/s only at 784K context (!!) makes a big difference.
It feels like a GPT-4 class model in terms of "stored knowledge" but is better at long-horizon tool calling than any of the GPT-4 class models.
Running on a 128GB MBP M4 Max, I'm getting ~24 t/s on generation and ~200 t/s on prefill. I was expecting it to feel slow, and it certainly does when e.g. generating code, but it's surprisingly useful as a "machine orchestrator" for simple tasks.
For non-agentic usecases, it's a decent enough model to converse with, and has the benefit of being entirely self-contained/private.
I am not convinced that the MTP setup for the QAT model adds very much in terms of speed on my M1 Max, but it is definitely worth experimenting with.
Fiddling about with local models has done so much for my conceptual understanding of what is going on.
FWIW and YMMV but I also found the Gemma 4 MTP head was occasionally breaking markup in Opencode, causing the thinking to display untidily and ultimately in some cases missing the stop token. So I've stopped using MTP there for now.
Recent Qwen 3.6 models have developer role support so it will occasionally surprise you with a structured multiple choice questionnaire.
I do enjoy their different personalities when they are tackling "explain this" type puzzles, though.
Gemma writes so well — like a concise code blogger. It makes you understand that the thing we hate about AI slop writing is specifically the cheesy, marketingese sycophantic ChatGPT tone. It's a choice to sound that way.
Qwen writes more tersely by default, like much english language documentation in Chinese open source projects. A couple of lines, code example, fact, code example, line of blurb.
I use this prompt every now and then with a new model. It's obviously a classic SQL puzzle but I've asked new web developers this in the past (prompted by discovering that a client's subcontractor didn't understand it and was therefore unable to migrate some code from relying on dodgy pre-MySQL 5.x behaviours)
—
I have a MySQL 5 table like this: [id, label, category, score]. It contains a list of items in different categories (text names like cat1, cat2, cat3) with a numerical score. Is there a way I can write a SQL query to find the item in each category that has the highest score, without using a subquery? No two entries in any category share a score.
I enjoy seeing what it deduces from the subtext.
Without "thinking" mode on, they always initially fail and you need to prompt them to find the answer. With thinking mode, they both produce really nice explanations.
For me, as an old freelancer who is pretty cynical about vibe coding or "agentic engineering", what I really want is an AI tool that can help me start to solve problems and help me find the right terminology or generate some boilerplate I can tinker with. Both of these models do fine at the kind of "starter" writing that I want when I am trying to untangle an idea.
https://pchalasani.github.io/claude-code-tools/integrations/...
I'm not Googling much of anything anymore. 9/10 times the information is awful, it's hard to parse out of whatever other spam it's surrounded by. Meanwhile, Claude will just do the thing one-shot or with a tiny bit of refinement.
The gateway to knowledge and getting stuff done is the LLM.
Google Search is a dinosaur.
It feels like we're living a century into the future. Not even smartphones were this cool.
https://newsletter.pessimistsarchive.org/p/when-educators-mo...
New decade, same old argument.
It's not
> "Claude, think for me"
It's
> "Claude, be my subordinate and get this done for me"
Instead of complaining on the sidelines, I'm getting a shit ton of work done.
Yeah, good ol' present for me too then, thanks.
An argument can be as old as the search engine and hold real value. There are ways in which unreflective search engine use has misled and mistrained people.
There’s always been argument to be had about how we manage and offload attention, what we gain and what we lose when resistance is reduced. It’s part of reflection that’s been necessary in order to make progress solid ground, and is more necessary with non-deterministic tech.
The phrase “Tactical tornados” may be older than web search and describes people who also got a lot done.
Models can be incredibly helpful boosters and situationally effective subordinates… and also patchy as a real engineering IC or org.
>"Claude, be my subordinate and get this done for me"
Since "this" is thinking, then the two formulations are equivalent.
>Instead of complaining on the sidelines, I'm getting a shit ton of work done.
Until you no longer have a job and are drowned in slop.
Nah, you are just producing a bunch of slop and hope that nobody notices.
It’s weird when people are proud of doing ton of work. Im the opposite, Im proud that Im doing minimal stuff without llms.
maybe you stopped thinking too much that you dont regonize that you are just producing slop that no one cares about.
AI is now getting humans to produce slop
The WALL-E chair-people future.
Thats the rub. I have an M4 with 48G. I wonder if it is worth testing this out.
My past attempts (with Ollama and various LLMs) were too slow to use.
If you're seeking the kind of hands-off claude experience, obviously not. They are slow.
If you want to learn how these things work, train them locally, tinker, play with the code, grasp the fundamentals, or just out of sheer bloody-mindedness and principle refuse to tether the functioning of your application to a cloud API...
I've dabbled with Qwen 3.x and Gemma 4 models a bit. They are alright but not that impressive. And my mac gets super hot if I use them for extended periods of time. It's just not very nice to use locally.
But if you just want to play around rather than code, you really might find the Gemma 4 12B model worth mucking about with just so you've gone through the steps. Especially if you want to muck about with image analysis or audio transcription.
If you're writing PHP I think you could even find it good enough. I've been modestly surprised. You can do that basic fiddling with the Edge AI Gallery app, which can enable thinking and has a customisable system prompt and some agent support.
You could also try the 14B Deepseek R1.
Honestly even if it is not good enough, if you are anything like me, I think you'll find that going through this process is really quite educational — it has made a lot of things more concrete for me in a way that I have found reassuring and valuable.
omlx + gemma 12b 6 bit + pi
it’s feasible for sure
MoEs for speed (qwen 35b, cohere 30b, gemma 26b)
Dense for more methodical work (qwen 27b [reigning champ], gemma 31b, gemma 12b)
MoE i recommend 5bit+
Dense i think 4 bit is okay
Play with your context size, you don’t really need that much, have lazy loading for tools and mcps
my pi extensions for anyone looking for a skinny quick setup, i have use `--no-skills` right now too:
"npm:pi-codex-goal",
"npm:pi-simplify",
"npm:pi-mcp-adapter",
"git:github.com/elpapi42/pi-minimal-subagent",
"npm:@wierdbytes/pi-statusline",
"npm:@aliou/pi-guardrails",
"npm:pi-lens",
"npm:@juicesharp/rpiv-todo",
"npm:pi-hashline-readmap",
"npm:@mrclrchtr/supi-review",
"npm:pi-cmux",
"npm:@mrclrchtr/supi-context",
"npm:pi-tool-search"
> NoSuchKeyThe specified key does not exist…
Also there are smaller, still usefull models that can run on 8GB or less.
One way or another local AI is the future. I actually find weaker models more interesting because it keeps me sharp (at the cost of velocity of course).
I run something very similar except for directly using pi as the agentic harness I use little-coder that wraps pi with reasonable defaults for running local models. Even though my local setup is a bit slow, it is a thrill to do real work completely locally.
Alas, this video appears not have been linked to the text that describes it. Perhaps I should ask an AI to generate an artistic rendering of the author's description.
It solve the "I'm coding on the plane and need to look up this thing I've forgotten" problem, for me at least
So there is no value in testing quality of answers, but there is value in testing token speed.
You just have to have correct expectations.
For me local models is all about quality, and how to achieve that - e.g. by providing guardrails that test the job done.
oMLX does the caching I need to fit models that are near gross memory, and it handles most of the work in finding usable models. After cobbling together various solutions over months, I now just use oMLX, often from Xcode. I can tell the difference between Gemma-4 (local/free) and Claude (paid) only on the largest tasks.
But there is an incentive not to use it if you want to write an article that uses only open-source tools, because it isn't.
Basically one has two real choices for local LLMs: llama.cpp (if single user) or vLLM (if multi-user/enterprise).
Plus a followup one where you see me type the question in and press enter (though that video is with Qwen 3.6, not Gemma 4) https://x.com/Freerunnering/status/2065354101878055038
harbor up omlx opencode
is this article old? It's not. I'm not sure why he went through all the bother of llama.cpp
Original video: https://x.com/Freerunnering/status/2065275403548168398
And in the blog post there is a table showing the different speeds I got from different engines.
Slowest combo was 38.1 tk/s, and the fastest was 72.2 tk/s. All from "the same" model.
Also Ollama has other issues (like forgetting what it really is - a wrapper).
When used as a verb, it should be "set up," and when used as a noun, "setup."
Other examples (verb, noun):
log in, login
back up, backup
shut down, shutdown
break down, breakdown
warm up, warmup