The biggest local LLM on your machine is useless if it can't call a single tool, no matter how many parameters it has
… It runs in under 1.5GB of memory with quantization. Is it going to match a Qwen3.6 27B on complex multi-step tasks? …
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Alibaba
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Alibaba Cloud plans to use fewer Western chips, to boost its already huge AI margins
Day 0 Support for Qwen 3.8 on AMD Instinct GPUs
… How many eggs does she sell?\nAnswer:", "max tokens": 64, "temperature": 0 Step 4 Optional — Accuracy Evaluation GSM8K MXFP4 on MI355X TP8, single node SGLang built-in benchmark: python3 -m sglang.test.run eval \ --port 9001 \ --model /mnt/models/Qwen3.8-2.4T-A95B-Quark-MXFP4 \ --eval-name gsm8k \ … …
Your old GPU can still run big LLMs – you just need the right tweaks
… Considering that quantization involves reducing the precision of model weights, Q8 can offer close to full accuracy of the model, but the VRAM hit is so massive that it makes sense to go for a higher parameter LLM and with heavier quantization. …
TurboQuant tackles the hidden memory problem that's been limiting your local LLMs
… Take Qwen3-Coder-Next as an example. …
I tested Qwen3 Coder Next against four other local AI coding models, and the gap was embarrassing
… It has 128 GB of VRAM for local AI workloads, and can be used for quantization, fine-tuning and all things CUDA. $3539 at Lenovo And one of them did it in five minutes Qwen3 Coder Next was the standout of all of these tests. …
I ditched Claude Code for a local model and a cloud supervisor, and it's twice as fast
… Qwen3-Coder only activates 3.3 billion of its 30.5 billion parameters for each token, which makes generation reasonably fast, but the entire model still has to fit into memory. You'd want to go ahead with a lower quantization and a shorter context if your machine doesn't have at least 16GB RAM. …
I stopped running the biggest local LLM that could fit, and a 2B model handles 90% of what I need
… Related Qwen3.5-9B tops every AI benchmark right now, but that's not how you should pick a model There's a lot more to a model than just benchmarks. …
You don't need an expensive GPU to run a local LLM that actually works
… But at Q4 4-bit quantized , you're looking at around 5GB when I'm running qwen3:4b. That changes everything. Even with a powerful GPU at hand, quantization can be the difference between running a smaller model and a larger one with far better capabilities. …
Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer | NVIDIA Technical Blog
… In practice, we recommend multinode training for faster training. torchrun --nproc per node 8 /opt/NeMo/scripts/llm/gpt train.py \ --name Qwen3-8B-nemo-depth-pruned-distill \ --devices 8 \ --num nodes 1 \ --tp size 8 \ --model path Qwen3-8B-nemo-depth-pruned \ --teacher path Qwen3-8B-nemo \ --legac… …
PrismML debuts 1-bit LLM in bid to free AI from the cloud
… The company claims that its 1-bit architecture avoids the tradeoffs that historically have accompanied low-bit quantization, specifically poor instruction following, errant multi-step reasoning, and unreliable tool use. …