Qwen3.7-Max: The Agent Frontier(qwen.ai) |
Qwen3.7-Max: The Agent Frontier(qwen.ai) |
Europe's sense of superiority and actual global importance/relevance is assbackwards.
Tiananmen Square is the first place to start.
Similarly, try talking to Nemotron about Epstein and see how quickly it shuts down.
What do you mean? This is not self hosted, it's closed source. And any website that targets China or is hosted in China will probably censor Tiananmen Square.
Hilarious thing to say when half this comment section is Americans giving so much of a fuck that they consider China-adjacent hosted models unusable due to the supposed risks. If what you were saying was true then those pragmatic Americans would just use whatever is most effective.
The Americans can cry about Chinese censorship and turn around and use Claude or Opus or Gemma or whatever, but the Europeans just throw a fit and then have to use one of the two anyway. And that whole crying about something while being completely helpless vis-a-vis doing anything about it is the definition of Europe so far this century. Globally irrelevant outside Germany.
"Tennessee man jailed 37 days for Trump meme wins settlement after lawsuit" and "The FBI Wants to Buy Nationwide Access to License Plate Readers"
Gotta love how the US is the bastion of free speech, justice and liberty!
Running Step 3.5 Flash locally for example, it's an amazingly capable model all things considered, but it's token efficiency is so bad that it gets out performed by most others wall-clock time (even with my MTP-support for it hacked in to llama.cpp: despite being trained on three heads, MTP 2 is the sweet spot, and only gets it from 20tk/s to 30tk/s on my Spark)
The DeepSeek models and Qwen 3.5 Plus are also good examples of this: compared to Opus, and especially GPT 5.5 they use many more tokens to get to the same answers.
I'm really hoping that Qwen 3.7 is better in this regard, can't wait to try it out
(ps. running DeepSeek v4 Flash on my Spark is absolutely wild, thanks antirez if you see this haha)
https://artificialanalysis.ai/evaluations/omniscience?models...
(had to add it to the chart, wasn't displayed by default. is it the lowest rate in the datasetor no?)
Note that a perfect "non-hallucination rate" is rather meaningless as such tests can contain human hallucinations.
It means the model aligns with the possibly-true, possibly-false beliefs of the group that made the test.
Or would you describe your methodology as more like picking a random sentence fragment as an input value then generating completions from your existing corpus without any post-input "learning" process related to the rest of the source material?
Totally understand why it may not be reasonable or in their best interest (and that the US is _absolutely_ not doing the same reflexively). But it would be lovely to be able to try these out on production workloads in earnest.
This even applies to OpenAI & Anthropic who don't even eval on the same datasets a lot of the time.
Even with LLMs, posts like this don't just fall out of a coconut tree. If you have a set of target benchmarks for your own model, then keeping "the set" of side-by-side comparable models is its own maintenance headache.
Realistically I assume they hope readers don’t notice the fine details.
The Qwen models are great for open weights but for every past release they haven’t performed as well as the benchmarks in my experience. They’re optimizing for benchmark numbers because they know it works.
The pool of people reading such articles while ignoring such details can't be big.
if they say it's 4.7 comparable, it anchors that into your head as the model to evaluate against.
I ran llama3:latest and it ran pretty fast! I’m curious to see how Qwen would run on my system.
(As a reference, DeepSeek v4 is severely throttled on these proxy services.)
Is this normal humans kicking the tires on a new model, or a few whales doing serious benchmarks?
Open-weight: Good enough for the majority of tasks, and I'm willing to spend a bit more time and effort steering towards my desired result.
4.7 is much better. But perception is a funny thing, once you think something is bad you start looking for it everywhere.
The setup I had to do was important and I had to compile koboldcpp with a few special params for my hardware, I mostly just had Claude figure it out. I don't remember everything I did now but it was very slow and would often stop mid task, it seems it was mostly a parsing issue. It made the model seem broken/dumb, but once I had all that settled I actually am able to use this how I use Claude Code. Disclaimer, I am pretty explicit with requirements, I imagine this fails more when you leave it to figure out things on its own but for my flow its pretty rad.
Currently setting it up as an automated agent now to pull Trello cards, create PRs for them, and move the card to be reviewed.
Command I am using to run: python koboldcpp.py \ --port 61514 --quiet --multiuser --gpulayers 999 --contextsize 262144 --quantkv 2 \ --usecublas normal --threads 4 --jinja --jinja_tools --jinja_kwargs '{"enable_thinking":true, "preserve_thinking":false}' \ --skiplauncher --model /data/models/Qwen3.6-27B-Q5_K_M.gguf --smartcache 5
It's very capable on almost any coding task I've thrown at it, and very good for easy-to-medium hard scripts, new code bases.
It struggles on some complex tasks in larger code bases, e.g. using to debug and fix bugs in llama.cpp it gets close to working code but often introduces errors. For such tasks its still very useful as a search/explore tool and drafting fixes.
Good balance of intelligence and speed.
I had a Google Pro account that I inherited from buying a Pixel 9 XL - it's free for a year after a flagship Pixel phone purchase. After a year they started charging for it, and i tolerated it, because Flash was usable in Antigravity for dumb auxiliary tasks that I did not want to waste GPT/Opus on. It had a separate generous quota from Gemini 3.1 Pro. Now with Flash 3.5 they combined the quotas with Pro, such that on a Google pro account you can work 4-5 hours per week in Flash. And by the way, 3.1 Pro is useless for programming, compared to Codex/Opus
I think they envision Pro plan as "just a taste of AI, enough to lure folks into the Ultra plan" but that won't work for me when Codex is half the price and DeepSeek 4 Flash is 1/10 of their price per task.
So I'll downgrade just enough to keep my Google Drive space. And use DeepSeek 4 as workhorse plus Codex or Copilot for advanced stuff.
> Oops! There was an issue connecting to Qwen3.6-Plus.
> Content Security Warning: The input text data may contain inappropriate content.
hey ChatGPT, how many civilians were killed in Gaza in the war since 2023?
> [one page of estimates from local and international sources with links]
I tried the qwen3.6-27b Q6_k GUFF in llama.cpp and LM Studio on my M2 MacBook Pro 32GB machine last week, and I barely get a token a second with either.
What sort of speed should I be expecting?
I tried some of the Llama 3 34b (nous-capybara?) models two years ago with llama.cpp, and I seem to remember getting a few tokens a second then, so not sure if I've got something completely mis-configured, or I just have unreasonable expectations.
Or maybe qwen 3.x is slower for some reason? (Is it mixture of experts?)
I'm not expecting it to be instant, but what I'm currently seeing is not really usable.
- A 27B "dense" model
- A 35B "Mixture of Experts" model, which activates only 3B parameters for each token.
For your hardware, I strongly recommend `unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M`. I have an M1 Max with 32GB VRAM from 2021 that can read at ~300-500 tokens/sec and write at ~30 tokens/sec with llama-cpp's default settings, which is plenty fast. The 27B model can read ~70tok/sec and write ~5tok/sec.
The 35B MoE model technically takes slightly more memory but is much faster because it's doing 1/9th the work. It's not quite as "smart", but it's comparable.
On testing I've done on same-quant apples to apples, with F16/F16 (ie, unquantized) kv cache, 35B-A3B underperforms against 27B on anything even remotely complex. But yes, 35B-A3B can be like 3-4x faster on my hardware.
By Qwen's own admission, on any meaningful benchmark (ie, ones that involve logic, math, or tool calling), 27B performs like 122B-10B and 397B-A17B, but 35B-A3B is somewhere between 27B dense and 9B dense.
Also, MTP recently got merged in, so I'd suggest downloading Qwen 3.6 MTP (I assume you get it from unsloth) and updating your copy of llama.cpp, and adding `--spec-type draft-mtp --spec-draft-n-max 2` to your arguments.
https://huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF/ https://huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF/
Also, I recommend not quantizing kv cache, and if you do, only quantize v. Lowering model quant while also lowering context size to fit F16/F16 or F16/Q8_0 massively improves model performance for thinking models. Also, quantizing cache, either k or v, decreases speed by a lot on some hardware.
I have a 24gb 7900xtx, so I can fit >32k F16/F16 context with Qwen3.6-27B, but use unsloth's Q3_K_XL. This performs better than Q(4,5,6)_K_XL with v quantized.
Edit: Oh, and since I mentioned Gemma 4, my testing mirrors my Qwen 3.5/3.6 experiences, 26B-A4B performs worse than 31B, but is also way faster. llama.cpp doesn't support Gemma 4's MTP style yet, so both could get even faster.
I tried the qwen3.6-27b Q6_k GUFF in llama.cpp
and LM Studio on my M2 MacBook Pro 32GB machine
last week, and I barely get a token a second with either.
(not sure of exact diagnosis/fix, but definitely look in that direction if you're still having this issue when you give it another shot)
Also, there are two stages - prompt processing, and token generation. Prompt processing is notoriously slow on Apple Silicon unfortunately. If you have large context (which includes system prompts, lots of tools loaded by a harness like Claude Code, OpenCode, etc) it can take minutes for prompt processing before you see the first output token. On the bright side, the tokens are cached between turns, so subsequent turns won't be so bad.
For comparison, I just ran a couple of quick benchmarks (default settings) with llama-bench:
Qwen3.6-35B-A3B at Q6_K_XL gave 858 t/s pp512 (prompt processing) and 43 t/s tg128 (token generation).
Qwen3.6-27B at Q4_K_XL gave 103 t/s pp512 and 8 t/s tg128.
That's the dense model, you probably want a mixture-of-experts (MoE) one.
Here's what you probably want instead: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF
/Users/gcr/llama.cpp/build/bin/llama-server
-hf unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M
--no-mmproj-offload
--fit on
-c 65536 # edit to taste
--reasoning on --chat-template-kwargs '{"preserve_thinking": true}'
--sleep-idle-seconds 90 # very aggressive: purge model from vram after this long
-ctk q8_0 -ctv q8_0 # Optional. Lower memory use, but lower speed. Omit if you can.
For agent harnesses, opencode is okay, as is pi or even Zed's built in agent panel. Claude code "works" with ANTHROPIC_BASE_URL=http://localhost:8080/v1, but is very chatty (the default system prompt burns 20k tokens). Crush (from the charm-bracelet folks) is particularly nice when starting out. I've personally converged on pi-agent under an otherwise-mostly-default setup. You can ask qwen to customize pi or write you an extension which helps a little.
You'll need to add `http://localhost:8080/v1` as an OpenAI-compatible model provider in your coding harness with any API key (doesn't matter) and any model identifier (doesn't matter with llama-cpp).
Note that pi doesn't have permissions. Everything is permitted. The hundred hungry ghosts you've trapped in a jar WILL find a way to delete your home folder someday. That's what Man gets for summoning demons without casting a circle of protection first. Flying too close to the sun etc etc etc
Take backups and then go have fun. Hope this helps.
Maybe that's underselling it. It is quite a good model and might end up replacing a lot of the work I was sending to Sonnet 4.6.
Also, Sonnet 4.6 is almost certain a much bigger model so the performance differences aren't unexpected.
This is not an open model
In an ideal world U.S. residents would use Chinese AI models and Chinese residents would use U.S. AI models.
Governments in both countries are collecting data for nefarious reasons. But the Chinese government has far less influence on a U.S. resident and vice versa.
We are all better off if our data is collected by a government halfway across the world instead of our own governments which hold incredible amounts of power over us.
China has more integration between intelligence and industry than many western countries, and it does present a higher risk of unwanted “tech transfer” to industry than running on oracle or Google or ms or Amazon does in the US.
DHS has long staffed full time agents in California to deal with foreign IP exfiltration - using qwen is like fast/easy mode for IP exfiltration: why make anyone get a job in your palo alto office when you can just send it to them in Hanzhou?
Upshot - If you have something proprietary you’re working on I would generally advise not to just direct send it to Alibaba.
This made me think of a Seinfeld episode: "I didn't know it was possible not to know that."
That's exactly the fear, and why would it not be logistically feasible? The threat is definitely a bit overhyped, but China has a longstanding track record of aggressive corporate espionage.
Even if they weren’t individually worried about their proprietary data being shared with Chinese domestic competitors or with government… their audit / security programs likely wouldn’t allow it for a _huge_ range of types of data.
I've created a 2.54BPW quant that fit on my hardware with 128k context, 20 tps tg and 200tps pp, while maintaining high scores on many benchmarks: https://huggingface.co/tarruda/Qwen3.5-397B-A17B-GGUF/discus...
Oddly enough, though, Qwen 3.6 35B A3B and Gemma got some really good reviews, despite being way smaller than any of these ones.
Qwen 3.5, 122B A10B: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-GGUF
Qwen Coder Next, 80B A3B: https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF
It's kinda weird that DeepSeek V4 Flash is supposed to be 284B A13B, but shows up as 158B in HuggingFace, probably some weird bug: https://huggingface.co/unsloth/DeepSeek-V4-Flash and that's not even just Unsloth but like the official source too https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash (so also doesn't fit the category unless you get a heavily quantized version to run, but cool regardless)
Mistral Medium 3.5 is interesting because it's 128B but dense, so probably too slow for most folks: https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF
GPT-OSS, 120B A5B: https://huggingface.co/unsloth/gpt-oss-120b-GGUF
I’m on an M1 Max with 32GB VRAM, so I’m looking forward to the 27B or 35B-A3B models. Is dropping $5k for an RTX 6000 or a DGX Spark really the best option?
https://marketplace.visualstudio.com/items?itemName=sst-dev....
It adds a button to VSCode to open a tab with opencode loaded. It's a bit better than just opening the CLI because it has some vscode integration.
With their $10/mo opencode go plan: https://opencode.ai/go
For my use it's about endless use of DS4 Flash on high setting. I find high better than max because it's less chatty.
The best thing is the speed. So many tokens per second.
edit: This is how it looks in action https://i.imgur.com/RNDXr07.png
On Hacker News I wonder if most people even opened the article at all most times.
e: which itself is a modification of RTFM from usenet
Your ID has been passed to Israel and your internalized "threat" rating number increased 300 units. Every packet you produce on the internet is now earmarked for 100 year retention.
EDIT: I run with context wired at 64K
Recommend https://www.reddit.com/r/LocalLLaMA/ as a great source for this type of discussion.
Obviously bigger != better but I don't know what the differences are.
* _0 and _1 do not use K quant and scales 32x32 blocks according to the original (B)F16 values; _0 scales the block using the original max and min values. _1 does this per row instead of per block.
* K quants do something similar, but now splits blocks into subblocks inside a superblock where the superblock has min/max scaling, but the subblocks also have scaling in the range of the superblock's scaling and are stored using less bits.
* K's M, L, XL are just how aggressively the subblocks and their scaling factors are chosen. Generally, it puts a max on how far you can deviate from the chosen quant to maintain the desired quality, but also gives them a bigger budget to perform that excursion in. XL most aggressively tries to preserve the intended quality, while S does the least.
* Dynamic quant on top of this scales entire layers, full of blocks, according to how much they effect various measurements (such as KLD and perplexity).
That said, there is no reason K_S is even produced by anyone, same with Q_0, Q_1, and I_NL. People should no longer be using those. M only is meaningful if you're trying to restrict the upper bounds: K_XL can reach BF16 for some weights, but rarely; people think this has a speed implication for hardware that has native 8bit in their tensor units (but it doesn't).
Unless you're specifically trying to cure a problem, stick with K_XL.
A lot of the content about AI out there is kind of produced to the lowest common denominator. Basically a never ending scheme of get rich quick/passive income kinds of AI content.
So I feel like that's exactly the right metric and the way to track it wrt hallucinations.
https://artificialanalysis.ai/evaluations/omniscience#aa-omn...
It rewards correct answers and penalizes hallucinations, and finally no reward for refusing to answer.
It's interesting just how poorly some popular Chinese models fare in this regard, like GLM 5.1 or DeepSeek 4 Pro.
Gemini 3.x has truly remarkable knowledge given how it leads in this benchmark despite being (quite a bit) more prone to hallucinate than Claude Opus.
But no, Google and OpenAI would rather always have an answer ready and tell you to mix glue into your pizza toppings :)
If by ROI you mean saving more money than using paid APIs, then I don't think it is worth it. All you gain is full sovereignty over your AI usage.
Running w/ Cursor and doing some "nights and weekends" type coding / conversations, I was hitting $100-200 of usage within a few weeks. I know there's probably better ways to manage costs, but I was getting enough value out of it to keep bumping my spend limit from $20 => $40 => $80 => $120 (and then I stopped spending! :-)
Messing around with local-llm, I've settled on `omlx` and `gemma` for "conversational", and I think it's `qwen-120b-a3b-6bit` or something for the "heavy hitter". Gemma "gets it" a lot more, whereas that particular `qwen` tends to fall into the "MuSt WrItE CoOooDeee!" behaviour in a lot of cases instead of holding a conversation, and does an awesome job of randomly spitting out ascii-art diagrams or including full-blown bash shell scripts to illustrate different cases.
My POV is: "Local for slightly slower/casual usage", the ~1% of battery usage per minute of LLM is shockingly accurate (eg: 30 minutes == 30% drop!). "Gemma for discussion and emitting DESIGN-... docs", and "Qwen for converting DESIGN-... to PLAN-...", (as well as implementation, but generally from a fresh context loading the relevant PLAN-... or supporting docs)
...then supplement that with direct Cursor usage in case I screw up some setting on being able to get the local LLM working, or if I need to include literal web-research or really having access to some SOTA model. Using the pi-coder harness locally, web pages are kindof a difficult conundrum as they can be kindof gigantic and are really worthy of special casing, some sort of sub-harness, etc... but the more "stuff" you put into the agent, the less context window (and memory!) you have available, so it's a real balancing act.
The other biggest problem is that you're limited (locally) to ~20-80tps and in some cases you have to chew on or "swallow" the whole prompt up to that point if you end up with some sort of cache miss (TTFT). The `omlx` server does a pretty good job (after you tweak some settings and stuff) of allowing MANY prompt continuations to nearly immediately start generated tokens, but sometimes if I have two agents going (eg: Gemma talking shit about Qwen's output or vice versa) in a longer context window, then you'll take that hit.
"Other people's compute" is definitely more freeing, but even looking at $200/mo usage that's $2400 vs. the ~$6k for a maxed out MBP. Call it $2500 vs. $7500 and you'd say that "local AI gives you a 3-year amortization window for a slower, worse experience" ... but if you're strategic about your usage, the ability to "talk for free" and occasionally "burst" to an online provider or having some hugging-face tokens to try out different models that you can't quite run locally is really nice. Talking to the AI (locally) to even just do non-coding planning without worrying about data leakage or privacy issues is phenomenal, and you end up owning a really nice laptop!
In some ways, seeing the "advantage" of having the local 128gb capacity for LLM, I'm semi-wishing I'd have gotten a mac mini instead, but then I can't quite do the 100% offline stuff (eg: coffee-shop) that the maxed out laptop allows.
If it were a mini running locally, I'd feel more comfortable calling it the always-on "AI brain" to process my emails, run crontab summaries, whatever kindof "open-claw-ish" stuff that you could do w/o relying on having to "keep the laptop lid open all the time". I'm sure there's ways to repurpose things, but longer-term, call it even 3-5 years from now... any sort of 128gb machine will be more than capable where you'd want to have one "doing stuff" locally within your home network (IMHO).
>"...if you're strategic about your usage, the ability to "talk for free" and occasionally "burst" to an online provider or having some hugging-face tokens to try out different models that you can't quite run locally is really nice. Talking to the AI (locally) to even just do non-coding planning without worrying about data leakage or privacy issues is phenomenal, and you end up owning a really nice laptop!"
^ this resonates, loudly.
But I was not super impressed with deepseek 4 flash using it from the official API either, so it doesn't seem quantization fault. It is a good model, but nothing out of the ordinary in the few benchmarks I ran on it (with full awareness that benchmarks are biased).
It's not nearly worth it to me to get an incremental improvement in performance if it means I have to move to hosted environments with Qwen 3.7 (or Claude or Gemini or whatever).
If you use a service outside your country, I believe you could have all your code stolen and get hacked/exploited in a way that would be totally legal.
As Americans go through life, some of them will become people with power. When you need to leverage that power, having the right knowledge about them can effectively transfer that power to you.
Tiktok was a goldmine, because every 20-something on their way to a future position of power was uploading every single facit of their digital life to CCP servers everyday.
On the other hand, there's other models where the source is 100% open, the training data is known, and people have reproduced the same model from scratch, so while those trail behind, there's definitely an effort to make models more open and capable.
I've certainly used these models without wifi without any differences.
A lot of people are purchasing access via Alibaba Cloud directly, or indirectly by companies which host the model.
Sure, that is until each government's dataset is interesting enough to the other to facilitate a data-sharing agreement.
There's gotta be an internet "law" that says something like "Eventually, the data you volunteer to a benign 3rd party eventually winds up being used against you by someone". This is short-term thinking at it's finest.
It's highly improbable that the US government has a secret team inside Anthropic and OpenAI manipulating their training regimen.
One: it would be relatively technically trivial for $GOVERNMENT_AGENCY to just monitor all the prompts + context we send over the wire to OpenAI/Anthropic/etc. That's a goldmine of sensitive personal and corporate data, no secret team needed (although, the LLM providers obviously would need to cooperate)
Two: Rather than secret infiltration teams influencing model training I think what's more likely on the training side of things is simply self-censoring by the LLM providers, so that they don't risk angering the government.
I highly doubt that China has government interlopers, secret or otherwise, inside Qwen's training team. Nonetheless, "sensitive" issues like Tiananmen Square are censored. I would imagine that much/most such censorship in China is self-censorship that doesn't leave a legal/paper trail. That's what we're in danger of seeing (more of) in America IMO.
I take this for granted given Room 641A https://en.wikipedia.org/wiki/Room_641A
Thus, I’ve pondered whether anything they’ve learned has changed the world / had a big impact (like on their understanding of human psychology, perhaps per region). They’ve heard phone calls, they’ve read emails, diaries get brought to court… but these are systems that would be used like diaries but also prompt users for more and more.
You don't need a secret team to manipulate whats coming from them: https://responsiblestatecraft.org/israel-chatgpt/
Are they? They don't behave like it.
It's not very subtle manipulation either; ask qwen of Taiwan is a part of China in German and in English and only the English answer will be party-approved.
I think it's borderline naive to assume various agencies haven't infiltrated OpenAI, Anthropic and others, essentially the entire world was wiretapped by NSA in the past, to assume they don't have an employee or two at these companies does seem a bit naive to me.
- Your RTX 6000 is closer to $10k now
- Sparks are creeping into the $4-5k range
- AMD Strix are ~3.5k
- Apple depends on chipset and memory. Sweet spot would be 128gb M3 Ultra, probably $6-8k but admittedly haven't been tracking closely. New M5 might come in the fall. You can get a new 128gb M5 Max laptop for ~5-6k today.
- a 4x3090 rig would take $5-6k
Every platform has tradeoffs, but it's mostly ecosystem, memory bandwidth, and power consumption. They're all slow. The best option is likely to rent hardware on Runpod. The RIO on self-hosting is very low unless you have a specific need or you're ok treating it as a hobby.
>The best option is likely to rent hardware on Runpod.
Vast.ai is much cheaper, but the broader point here is contestable. The only dimension in which cloud GPU rentals win is cost. You lose the confidentiality, integrity, and availability benefits of local deployments.
Not that I'd encourage anyone to throw large amounts of money to have access to LLMs, but you're definately going to be better off buying something that you can amortize over multiple years with a multi year warranty.
This whole thing is really starting to remind me of the crypto hype phases of 2016-2018 when everyone thought their investment in GPUs was going to make them rich.
Yes, LLMs are sloppy, and local models usually more so (but things change fast).
But the local ones have one big advantage: they are private. So you can safely feed them the collection of your private documents and things you wouldn't trust people like sama with. The fact that some people do not care is one of the failures of our educational system.
In October/2024 I got my Mac studio M1 ultra with 128G, IIRC it was ~$2500. With recent prices explosion, it has certainly gotten more expensive. https://frame.work/ is selling 128G strix halo mainboard for $2700, but you have to add storage and case.
Unfortunately, the prices rose on these a lot, but unevenly. Beelink GTR 9 Pro is $4400, Framework Desktop is ~$3500, for what is basically the exact same mainboard as a Bosgame M5 for $2800.
Apple's M5 Max is another attractive option. Apple silicon traditionally had great MBW and was good at TG, but struggled with PP, but the new neural engines in those GPU cores have made a big difference in a good way here.
Gorgon Halo is rumored for June announcement with Q4'26 release with basically +100 MHz clocks on Strix Halo, LPDDR5X-8533 instead of LPDDR5X-8000, but more importantly, 192 GB max instead of 128 GB.
I'd say it's better to wait for Gorgon Halo than to grab Strix Halo now. However, Medusa Halo, rumored for H2'27, is slated to have up to 26c Zen 6 (heterogeneous cores - kinds funny that AMD is heading towards these as Intel retreats from them), 48 CU of RDNA 5 instead of 40 CU RDNA 3.5, and a 384 bit bus w/ LPDDR6, which should make 256 GB at more like ~490-600 GB/s MBW, which will really make Strix and Gorgon Halo obsolete.
Also worth keeping an eye out for Serpent Lake (intel CPU + nvidia iGPU on a single board with unified memory, rumored for 2028-2029 iirc), and on the 160 GB Crescent Island Intel dGPU.
Non-NVIDIA backends tend to get less support and new features land slower, or features that are expected to improve performance wind up hurting it instead. That sort of thing.
For basic “token in/token out” workloads without fine tuning, it’s probably fine ??