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https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-3 AA shipped a new benchmark last week as part of the Intelligence Index v4.3 update — a brand-new private eval that replaces τ³. Astra was farming a ton of points on it and used those to get even with Fable, but…
From ex Qwen leader https://x.com/JustinLin610/status/2098989619576729924 submitted by /u/jacek2023 [link] [comments]
Using the RTX3090 on a linux machine I built for it and running Qwen 3.8 27B getting average 100t/s compared to my MacBook 20t/s I think I can finally get rid of my Claude subscription, this is good enough for me. I am a software dev and I can get what I need from this set up and be more productive.
Does anyone think the gurus on the DGX Spark forum are going to figure out how to magically fit DeepSeek 4.1 Flash on a 2x cluster, or is it only possible on 3 or 4 Sparks? submitted by /u/Porespellar [link] [comments]
VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39 tok/s (3 hours constant running xhigh) - 10K, 42.3 tok/s (16 runs) - 90K, 34 tok/s (16 runs) P
I'll start by saying I'm not talking about the models themselves, I'm aware that I can't come close to something like Fable's intelligence locally. Just wanted to get that out of the way. Basically. Over the last year I've gotten quite comfortable with claude code, and it seems likely there were be
It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram for all Upcoming models. Even for big models. Engram - Heard that approximately 1/3-1/2 of Model
This is an evolution on top of Raymond's KV cache streaming fork - all credits to what enabled this goes to him. The basic idea behind what he enabled was a pool of memory in VRAM that is used differently depending on the phase (prompt processing or decoding) and when total used context is lar
I'm not fond of having an irreplaceable subscription on some OpenAI and Anthropic and being eventually being unable to do my job without using tools from some sketchy corporation, so I kinda turned my attention to local models. I periodically tried and found some usefulness in them, but this y
from internlm: We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combin
Since we know Qwen 3.8 27B thinks quite long, but gives at least a good one-shot result where you can leave it to do everything on it own, how does it compare to the older series of models for very simple tasks where you don't want to think so long? The only fine-tune of Qwen 3.6 I genuinely enjoyed
submitted by /u/rm-rf-rm [link] [comments]
A few people here mentioned interest in a way to test their custom Pi setups, so I figured I’d drop this here: RoastMyHarness The basic idea is a small engine that sets up an environment to run DeepSWE benchmark tasks using bare Pi as a control and a variant of your choice, your Pi harness, an exten
While browsing a benchmark list site, I spotted a recently published 33B parameter model which claimed to beat Qwen3.8 27b on the ArtificialAnalysis (AA) intelligence index. I was obviously excited. But then, while looking into it, confusion starts to settle in. Their HF model is now listed as
I have been battling with my Qwen3.8:27b setup on my rtx 5080 16gb. I am using llama.cpp to run a nvfp4 version of qwen3.8:27b llama-b10699-bin-win-cuda-13.3-x64\llama-server.exe -hf williamliao/Qwen3.8-27B-NVFP4-GGUF:NVFP4 --jinja --chat-template-file d:\qwen\chat_template.jinja --reasoning-format
As you all know, Qwen3.8 Flash Next on mainline llama.cpp is still in a pretty experimental stage, but a lot of community forks are trying to get it to work better. There's also a closed-source solution called Halogen (https://github.com/peonist-ai/halogen-flash-server) that boasted 1.2k t/s prefill
Reports of the demise of coders may have been exaggerated. submitted by /u/SteppenAxolotl [link] [comments]
First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for deploying local AI on my organization’s H100s, and we are forbidden by management from running any C
https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF I used this draft model with https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw it averaging about 60 tokens per second tg speed on the 16 GB RX 9070 XT. This is much better than
Hey everyone! I'm curious to hear from people that use a combination of cloud-based frontier models and local ones for development. I'm planning to set something similar up and wanted to hear about actual examples of this in action. Currently my plan is to use my chatgpt plus subscription purely for
I have enabled the vision for the CIRU Strix UL4 quant of Qwen 3.8 flash next (others quants likely perform very similar) and tried it on a few things, then wanted to show my partner how great it works and she asked it it could identify plants. So I took a photo from a plant that we recently got as
Applied science work, from workflow design, data pipeline, results analysis, article/reports writing and data publishing online. 5 projects I did in the past replicated from start to finish. 3x to 4x more total wall time. Yes, HUGE toll on how much you can do in a day if this was the only mod
I find this new model at HF: "Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding. Architecture Agnes-3.0-Flash is a hybrid-attention decoder: three of every four layers run a gated delta rule (recurrent,
Q2 is there and Q4 is uploading as I type. Has his github been updated yet? How do you run this? https://huggingface.co/antirez/deepseek-v4.1-flash-gguf/tree/main submitted by /u/Queasy_Asparagus69 [link] [comments]
For those using these models for coding in larger projects where things can get complex, do you find yourself using the 8-bit quants if you have enough memory? Or do you stick with UD-Q6_K_XL? The 6-bit is faster, noticeably so on my setup. And I keep seeing people say it's imperceptible. I've been
For many, Vram is precious, I see many people recommend using --no-mmproj-offload to save gpu vram but it is painfully slow. Especially if you are using it with agentic coding. If possible, add that secondary gpu just for mmproj with --mmdev CUDA1(your gpu). It will be a magnitude faster than --no-m
Hi! I run both models on MTPLX on my m5 max, and since I have 128GB of ram I run the q8 27b. I think MTPLX only lets me run "optimized for speed" which it says is a dynamic q4 with 8 bit attention. Both of them honestly are very speedy! For coding (in pi agent in nodejs) I've just noticed that 27B
Hey gang, im wondering if you in theory could use ngrams as seen with Qwen 3.8 Flash or DS4.1 in order to dynamically train the model? Normally the ngram embeddings behave similar to a lookup table of sorts. So instead of every token having to be represented only inside the main model weights, the
i've been thinking about this more after trying different agent setups. the model isn't the only thing that determines how well an agent performs. The harness around the model matters a lot too. With a managed agent setup, you're often giving up control over things like the agent loop, context manag
Some people says terminal bench reflects model intelligence better than the intelligent index. From the look of it, the ranking does seem to reflect how people feel about the open and closed models. For the open models, GLM-5.3 is in a league of its own. GLM-5.3-Flash is leading the current g