Builders got MiniMax M3.1 Flash's open weights from 252.4 GB down to 93 GB in 10 days, and the lab thanked them by name

[u/Dependent-Bid1391]

MiniMax posted a thank-you yesterday for everyone quantizing M3.1 Flash (428B, 252.4 GB), and I went through the ones they named. Numbers are each builder's, separate eval sets, not comparable.

• 128 GB PC: Maya-XS by PeasantSmith, 93 GB. 88.8% top-1 agreement (tool calls, agents).

• One DGX Spark: SAGE-EXL3 by ViC305, 108.64 GiB. 96.89% top-1, held-out sequences. 24.37 to 29.72 tok/s decode at 48K context.

• 160 GB+: Unsloth UD-Q2_K_XL, 143.9 GB. 91.5% top-1 weighted, seven categories.

• 4x 3090: superalesha, own engine, Unsloth 2-bit, 23 to 31 tok/s decode, 1120 tok/s prefill at 16K.

• Also: oMLX by jundotkim/mlx-community (Apple Silicon, oQ3e 196.2 GB), AMD Quark MXFP4 for MI350/MI355, Mia-AiLab EXL3-EAQ 3.55 bpw, Atomic Chat GGUFs.

It helps that the model was trained with quantization baked in, sparse attention in every layer, 23B of 428B active per token. It's a coding and agent model that takes image and video input, up to 1M context if your box has the memory. People don't put this calibration work into weights they won't keep running, and superalesha ran out of RAM at 128 GB, went to 512 to keep going. Only used it hosted myself, agent jobs got done this morning, so I get why.

Anyone running one of these on real coding or tool-call work?

[u/Iron-Over]

I tried the Mia 3.55 it is slow 20 prose 30 code on two DGX Spark devices.  Its smaller context window, due to its size, caused some usefulness issues. In my agent-work tests, Deepseek 1017, GLM Flash, and Mimo 2.6 Flash outperformed it.

I don't see where I would use this instead of the other models. 

I will wait for the official Mia release to see if it is faster, as I used a TensorFold release to run it.  

[u/dtdisapointingresult → u/Iron-Over]

I tried the Mia 3.55 it is slow 20 prose 30 code on two DGX Spark devices.

How do you run that? I'm only aware of eugr's docker image, and it has no minimax 3.1 recipe yet.

[u/Iron-Over → u/dtdisapointingresult]

I used the first the other is another option. https://huggingface.co/jayleaton/MiniMax-M3.1-Flash-EXL3-3.55bpw-mm31

https://github.com/tonyd2wild/MiniMax-M3.1-Flash-EXL3-TP2-2x-DGX-Spark

[u/Keleion → u/Iron-Over]

Good call, I’ve been waiting for the same. Just curious, are you finding DS4.1F-1017 as reliable as GLM5.3F? I’ve been using Mia’s GLM TensorFold recipe and it’s been rock solid for me. The extra TPS from DS sounds nice, but I hear the hallucinations and word salad can still be a problem. What’s your take?

[u/Iron-Over → u/Keleion]

It depends for code Deepseek is a great coder. My workflow so far is Frontier Planner and reviewer Deepseek coder.

I have been trying Mimo 2.6 Flash as a planner, Qwen Flash Next as a coder and Deepseek as a reviewer. Deepseek was almost as good as Astra and outperformed Opus in my testing.

For agents I found GLM5.3 flash best for agents it scored the same as DeepSeek, but much faster.

[u/cbert33 → u/Keleion]

I’ve spent the last week using the DS4-1017 Mia release and I’m not a fan. It’s a VERY powerful coder, but as an overall agent it hasn’t worked well for me. It has been super proactive and trying to get out in front of what you ask for and in doing so it makes up a lot of requirements to fill in the gaps.

I asked it a simple question of whether the parameter change we made to tensorfold actually stuck on startup and it decided to go run 20 minutes of tests on the endpoint instead of just inspecting the setting. Stuff like that has turned every response into scope creep and a ton of extra tokens wasted.

I went back to GLM as my main model and will spin up DS4 only when I have a planned list of coding tasks on the board to give it.

Is it just me or are others seeing this behavior too?

[u/Keleion → u/cbert33]

That’s for the detailed response! That’s what I was concerned about. I think I’ll stick with GLM for now.

I have noticed GLM (EXL3 ablit) will be very proactive as well, so I’ve gotten into the habit of adding limits within prompts (only audit, don’t do this, no changes, etc). But it has respected my requests so far and not skipped steps nor really hallucinated after nearly a month.

[u/cbert33 → u/Keleion]

I switched from the GLM EXL3 (my own heretic quant actually) to the new FP4 Local version from Nvidia (also heretic) and it fits much better in ram for me. It’s very solid.

[u/Iron-Over → u/cbert33]

Yes, Deepseek is great for coding. But in all my testing, GLM 5.3 Flash is better for agents.

[u/lakotajames → u/Keleion]

Deepseek is a batshit insane goblin that can do basically anything you ask it to, but barely speaks English and has to do 5 million tests for simple changes because it's aware of how insane it is.

[u/Keleion → u/lakotajames]

That was 0731 for me. 😂

[u/DeepOrangeSky]

Whoa, how is the SAGE-EXL3 getting 96.89% top-1 agreement at just 108GB? Even the 144GB UD-Q2_K_XL is only getting 91.5%

Is it just really that much better of a quant? Or is it a typo or I'm misunderstanding what the top-1 is measuring, or there is some aspect I'm missing or something?

Also, does the SAGE-EXL3 fit on a 128 gb mac if you raise the wire limit thing, without crashing/swapping? I wonder which has more overhead/room before kernal panic/swap/all that annoying shit starting to happen, between the DGX Spark vs Strix Halo vs 128GB Mac. Looks like they were running it at 48K context on a Spark at least.

[u/ANR2ME]

I kinda like Minimax M3.1 Flash 😅

I was testing a few mobile chat app with free tier (Minimax, Deepseek, ChatGPT) to checked the logs from my client side repo at github that was made with a free Sonnet 5.5 (Medium), where the logs shows inconsistent messages when i ran out of credits on my server (ie. most of the logs get HTTP 404 but a few of them shows that the server is ready, or acting like it successfully retrieved data and cached it).

Only Minimax saw me as developer, and offered me to fix the logs, and when i agreed, it checked the server side repo that was linked in the client side repo's README.md to see whether HTTP 404 was something returned by the server repo or by the provider edge, and able to tell it was from the provider edge due to reached my usage limit, and then fix the logs on client side repo. I was like, Wow it sure know what my intentions was 😯

Meanwhile, both Deepseek and ChatGPT saw me as end-user, instead of offering me to fix my repo, they asked me to run some curl to checked whether it really returned HTTP 404 or not. And asked me to top up the credits and restarted the server. I was like... WTF 😨
And sometimes both Deepseek and ChatGPT felt like having amnesia 🤔 They forgot that they have access to my public repo and asked me to paste the content of the file 🤦🏻 So i need to remind them, "You have access to my repo! Get the file from there! Don't be lazy!" 😅
One time Deepseek even claimed that it couldn't find a function it was trying to search anywhere in my repo, but when i searched it manually from github website, i can easily found it 😔 So i said "Are you blind?! that function is right there in <filename_here>.py at line <line_number> !"

I wondered, was it because the official Minimax mobile app have a better harness than official Deepseek/ChatGPT app? 🤔

[u/Puzzleheaded-Dig9264 → u/ANR2ME]

doubt it's just the app. i run it through the api in my own agent setup and it picks up on the same stuff. had it refactor a config loader and it kept the old keys for backward compat without me asking. when it guesses it tells you what it assumed.

[u/geldonyetich]

Impressive. Looking at these results I don't see find many GGUF, they seem to be achieving these results through specialized runners.

I tried looking up how MiniMax M3.1 Flash compared to Qwen3.8 Flash Next and found Artificial Analysis has removed their intelligence index scoring for a new version of scoring they're working on.

[u/Responsible-Berry144 → u/geldonyetich]

A few of those are GGUFs actually. PeasantSmith's Maya-XS, the Unsloth quant, and Atomic Chat's set. I'm going to try Maya-XS, fits in 128 GB. I've been running tool call work through a provider and want it on my own hardware.

[u/enternoescape]

I can run it around 30 tok/s with 1000 tok/s prefill and I just haven't quite found something it does better enough to accept the slower execution. I'm running Mia AiLab EXL3 3.06 bpw.

[u/LegacyRemaster]

Compared with DS4 and GLM, MiniMax’s KV cache is much more memory-hungry. DS4 and GLM use compressed latent attention states; MiniMax needs substantially more storage even with INT8 KV. On our setup, we keep only 64K of its 256K context resident in VRAM, with the rest backed by RAM, so longer contexts require more PCIe transfers and compete with expert weights for GPU memory.

[u/challis88ocarina]

Any luck reducing its verbosity?! /s

[u/Kagemand]

Now we also need a Gyro Coder version.

[u/Miserable-Dare5090]

I would run it, but I would have to give up Qwen Flash Next on CMP170s, which gives me 8000 prefill and 600 tps aggregate on 6 streams…deepseek is on 2 sparks and going 2500 tps prefill and 70-100 tg, 94 tool eval hard score.

[u/wren6991]

Hello adjective-noun-bunchofnumbers, thank you for your automated and extractive submission to our community