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Three weeks away and the leaderboard reshuffled. Kimi K2.6 brings 1T parameters under open weights, Qwen 3.6 stays the consumer GPU king, and DeepSeek V4-Flash proves too hungry for single-card setups.
DeepSeek returns with a 1.6T MoE monster under MIT license, Gemma 4's 31B dense model climbs to #3 on Arena AI, and ICLR 2026 papers point to what's next for local inference.
Qwen3.6-27B scores 77.2% on SWE-Bench Verified with a dense architecture that fits on a single RTX 4090. The MoE efficiency narrative just got complicated.
Alibaba drops Qwen3.6-35B-A3B with 73.4% on SWE-Bench Verified and Apache 2.0 licensing. The 3-billion active parameter class now has three serious contenders.
NVIDIA's Nemotron 3 brings a hybrid Mamba-Transformer architecture to consumer GPUs while Meta abandons open source for proprietary Muse Spark. The open-weight field just reshuffled.
Google, Alibaba, Meta, Mistral, OpenAI, and Zhipu all ship competitive open-weight models under permissive licenses. The battleground shifts from benchmarks to inference speed on your actual GPU.
Google's Gemma 4 lands with Apache 2.0 licensing and benchmark-topping scores. But a nasty inference speed problem means Qwen still wins on your actual hardware.
Mistral Small 4's 119B MoE unifies reasoning, vision, and coding—but needs datacenter hardware. Qwen 3.5 35B-A3B remains the consumer GPU king at 112 t/s.
MiMo-V2-Flash runs 309B parameters on RTX 4090s. GLM-5 sets benchmarks but needs datacenters. Llama 4 Scout stays out of reach.
Qwen 3.5's MoE models hit S-tier benchmarks, NVIDIA's Nemotron 3 Super delivers 5x throughput gains, and GLM-4.7-Flash brings frontier coding to consumer GPUs. The open-weight race just accelerated.
Which local models can actually use tools, call functions, and run multi-step workflows? BFCL and TAU-bench scores from 8GB to 32GB VRAM.
Head-to-head comparison of local chat and assistant models from 8GB to 32GB VRAM. Benchmarks, real speeds, and honest assessments of what your GPU can actually run.
Which open-weight coding model should you run locally? HumanEval, SWE-bench, and real-world tests from 8GB to 32GB GPUs, with setup instructions for IDE integration.
From TranslateGemma to LLM-based translation with Qwen and Aya Expanse. Privacy-first alternatives to Google Translate and DeepL, tested per GPU tier.
Run image analysis, document OCR, and visual reasoning locally. Qwen3-VL, Gemma 3, Phi-4 Vision, and more tested from 8GB to 32GB VRAM with real benchmarks.
Complete guide to running local AI on 12GB GPUs - chat, coding, translation, vision, speech, and agents. The comfortable tier for RTX 3060 12GB and RTX 4070.
Complete guide to running local AI on 16GB GPUs - chat, coding, translation, vision, speech, and agents. The sweet spot for RTX 4060 Ti, RTX 5060, and Arc A770.
Complete guide to running local AI on 24GB GPUs - chat, coding, translation, vision, speech, and agents. Where local models start competing with cloud APIs. RTX 3090 and RTX 4090.
Complete guide to running local AI on 32GB GPUs - chat, coding, translation, vision, speech, and agents. The new frontier with RTX 5090. Near-lossless quantization and 70B models on a single card.
Complete guide to running local AI on 8GB GPUs - chat, coding, translation, vision, speech, and agents. Model picks, benchmarks, and honest limits for RTX 4060, RTX 3070, and similar cards.
Forget 700B parameter flagships you can't run. Here are the open-weight models that deliver real performance on consumer hardware - with actual benchmarks.
Alibaba's new 35B model matches Claude Sonnet 4.5 on benchmarks while running locally on an RTX 4090. Here's what you need to know.
