Local LLM News: February 2026 Update
February 2026 is proving to be an exciting month for the local LLM community. Here’s what’s been generating the most discussion on r/LocalLLaMA.
MiniMax M2.5: The Open-Weight Counterattack
The biggest news this month: MiniMax M2.5 has taken the community by storm. Released by Chinese AI startup MiniMax, this open-weight model scored higher than proprietary models across multiple benchmarks including coding, agentic tasks, and search.
The post gathered over 362 points on Reddit, sparking active discussion about “open-weight models finally catching up” to their proprietary counterparts. This represents a significant milestone - for the first time, an open-weight model is competing at the top tier across multiple benchmark categories.
Key highlights:
- Outperforms proprietary models on coding benchmarks
- Strong performance on agentic tasks
- Competitive search capabilities
- Available as open-weight (not just open-source)
Qwen3.5: Alibaba’s Agentic AI Entry
Qwen3.5 was officially released on February 16, 2026, marking Alibaba’s entry into the agentic AI era. The first model in the series is Qwen3.5-397B-A17B, a Mixture-of-Experts (MoE) model with some impressive specs:
- 397B total parameters with only 17B activated per task (A17B)
- 60% lower inference costs compared to Qwen3.0
- 8x improvement in workload throughput
- 201 languages supported
- 1M context window
- Native multimodal capabilities for visual agentic tasks
The model scored 87.8% on MMLU-Pro, placing it among the top-tier models. It’s designed for autonomous task execution across mobile and desktop applications.
Community reaction has been mixed - while the technical achievements are impressive, some users note that the “open-weight” license terms may not be as permissive as truly open-source alternatives like Llama.
DeepSeek V4: The Coding Monster Leaks
Leaked benchmarks for DeepSeek V4 suggest it could outperform Claude and GPT on coding tasks with 90% HumanEval scores and 1M token context. Expected mid-February 2026, this model threatens to upend the industry again.
According to insider leaks and recent research papers, V4 represents a massive architectural shift focused on:
- Long-context coding mastery
- Extreme efficiency
- 1M token context window
The community is closely watching this release, as DeepSeek has consistently delivered impressive results at competitive price points.
llama.cpp Gets Distributed Inference
A major technical advancement: llama.cpp now supports distributed inference across multiple machines. The RPC code from gggerganov was merged, allowing you to run a model across more than one machine.
Current limitations:
- Limited to FP16 (no quant support yet)
- Doesn’t work with Vulkan yet
- Still a work in progress
This is a game-changer for users with multiple GPUs who want to run larger models by distributing the computational load.
Hardware Economics: DDR5 RDIMM vs RTX 3090
A discussion on Reddit revealed a fundamental turning point in local LLM hardware selection: DDR5 RDIMM pricing per GB has dropped below RTX 3090 VRAM pricing per GB. The post garnered 392 upvotes.
This changes the economics of running local LLMs:
- System RAM is now cheaper per GB than VRAM
- More RAM means larger context windows
- May shift hardware priorities for local AI enthusiasts
LLM Explorer: 15,000+ Models Listed
The LLM Explorer now lists over 15,000 LLMs with all the latest ones from HuggingFace. New features include:
- Omni-search box
- Multi-column filters
- Detailed model properties on separate pages
What’s Next?
The local LLM space is moving fast. Key themes this month:
- Open-weight models are competitive with proprietary AI (MiniMax, Qwen3.5)
- Agentic AI is emerging as a major capability (Qwen3.5)
- Coding capabilities are improving dramatically (DeepSeek V4)
- Hardware economics are shifting in favor of more accessible local AI
- Distributed inference is becoming viable (llama.cpp)
Stay tuned for more updates as these stories develop!
Want to discuss these developments? Join the conversation on r/LocalLLaMA.