Qwen Developer Hints to Skip 35B-A3B: Three Community Theories Explained

An Intriguing Developer Hint

Recently, a message circulating on Reddit's LocalLLaMA community sparked heated discussion: a developer from the Qwen (Tongyi Qianwen) team suggested that users "don't need to wait for the 35B-A3B model anymore." This seemingly simple statement sent ripples through the open-source LLM community—what does it actually mean? Is a more powerful model on the horizon, or has this product line been quietly shelved?

For developers who closely follow open-source model iterations, any official hint from a frontline team deserves careful interpretation. In the fiercely competitive LLM landscape, decisions about model specifications often reflect a team's deeper judgment about technical direction and market demand.

It's worth noting that r/LocalLLaMA is one of the most active communities worldwide for local LLM deployment, gathering a large number of developers and enthusiasts focused on running open-source LLMs on consumer-grade hardware (such as RTX 4090, Mac Studio, etc.). The community has an extremely keen sense for discussions about quantized model versions, inference frameworks, and real-world performance across different model specifications. Any passing remark from a leading team's developer gets quickly amplified and analyzed.

Decoding the MoE Architecture Naming Convention Behind "35B-A3B"

To understand this message, you first need to decode the naming convention. "35B-A3B" follows the typical MoE (Mixture of Experts) architecture naming pattern:

• 35B refers to the model's total parameter count of approximately 35 billion;
• A3B where "A" stands for "Activated"—the number of parameters actually activated during each inference pass, approximately 3 billion.

The core idea behind MoE architecture originated from the mixture of experts paper by Jacobs et al. in 1991, but it truly shone in large language models after 2022. The working principle is: the model consists of multiple "expert networks" (typically feed-forward neural network layers), and during each inference pass, a "router/gate network" decides which experts to route the input to. For example, a model with 64 experts might only activate 2-4 of them per inference pass. Google's Switch Transformer, GShard, and Mistral's Mixtral 8x7B are all representative implementations of this architecture.

The core advantage of this design lies in decoupling model capacity from computational cost: total parameters determine how much knowledge the model can "remember," while activated parameters determine the actual compute consumption per inference. The model possesses vast knowledge capacity (35 billion parameters) but only activates a small portion (3 billion) during actual operation, thereby maintaining strong capabilities while significantly reducing inference costs and memory usage. Qwen's previously released Qwen3 series adopted a similar "total parameters–activated parameters" naming logic, such as the widely popular 30B-A3B model.

Therefore, 35B-A3B was originally seen by the community as a minor upgrade to the existing MoE model—total parameters bumped from 30B to 35B while keeping activated parameters at the 3B level, improving capability without raising the deployment barrier.

The Evolution of the Qwen Series

Qwen (Tongyi Qianwen) is an open-source large language model series developed by Alibaba's DAMO Academy. From Qwen1 in 2023 to Qwen2 and Qwen2.5 in 2024, and Qwen3 in 2025, the series has seen its influence in the open-source community grow steadily. The Qwen3 series was the first to introduce MoE architecture at scale, launching multiple MoE variants including 30B-A3B (approximately 30 billion total parameters, ~3 billion activated parameters), complementing dense models (such as 0.6B, 1.7B, 4B, 8B, 14B, 32B). On platforms like HuggingFace and Ollama, the Qwen series has risen to the top ranks globally in downloads and community engagement, standing alongside Meta's Llama series and the Mistral series as the three major open-source LLM families. It's precisely this industry standing that makes any product planning adjustment from the team attract widespread attention.

Three Community Theories About Qwen's Next Move

The developer's "don't wait" statement prompted the community to speculate in multiple directions. Based on Reddit discussions, here are the main possibilities:

Theory 1: A Larger-Scale MoE Model Is Coming

The most optimistic interpretation is that the Qwen team skipped the 35B intermediate specification and is preparing to release a more competitive larger model directly. Some users speculate it could be a 122B-level MoE model. If true, this means Qwen aims to compete directly with higher-end open-source models rather than making incremental iterations at medium scale.

If Qwen indeed plans to release a 122B-level MoE model, its direct competitors would include subsequent versions of Mistral's Mixtral series, DeepSeek's DeepSeek-V3 (671B total parameters, 37B activated parameters), and potential MoE variants of Meta's Llama series. In the open-source space, larger total-parameter MoE models mean stronger knowledge coverage and reasoning capabilities, while if activated parameters are well-controlled, they can still run on relatively affordable hardware. Competition at this specification level isn't just about benchmark scores—it's about real-world task performance in long-context understanding, code generation, multilingual capabilities, and more.

Theory 2: Product Line Strategy Adjustment, Focusing on Core Specifications

Another interpretation is that the team believes the existing 30B-A3B already adequately covers this segment's needs, making a separate 35B-A3B release largely redundant. Rather than investing R&D resources in similar specifications, it's better to concentrate efforts on more differentiated products. This aligns with the common strategy of mature teams to "avoid product line redundancy."

In real-world LLM applications, different specifications correspond to distinctly different deployment scenarios and hardware requirements. Models at the 3B activated parameter level can typically run on consumer GPUs with 8GB VRAM or on mobile devices; 7-14B level requires 16-24GB VRAM; dense models above 30B typically need multi-GPU setups or professional-grade A100/H100 cards. While MoE models have fewer activated parameters, the total parameters still need to be loaded into memory or VRAM (to allow expert switching at any time), so their memory requirements fall between a dense model matching the activated parameters and one matching the total parameters. For example, the 30B-A3B model at 4-bit quantization requires approximately 16-20GB of memory, which happens to fit a MacBook Pro with Apple Silicon or a single RTX 4090. The capability gains from a 30B to 35B total parameter increase may not be enough to deliver a qualitative leap for users on the same hardware—this may be one reason the team abandoned this specification.

Theory 3: 35B-A3B Was Never on the Official Roadmap

The most conservative possibility is that 35B-A3B was never on the official roadmap and was merely wishful thinking from the community. The developer's statement was more of a reminder to "stop waiting for something that doesn't exist."

What This Message Reveals About Open-Source LLM Trends

Regardless of the final answer, this message reflects several key trends in current open-source LLM competition.

MoE Architecture Has Become a Mainstream Technical Direction

Through the "large capacity, small activation" design, teams can deliver stronger capabilities within limited inference budgets—particularly important for local deployment and edge scenarios. Qwen's continued investment in this direction shows that the architecture's cost-performance advantage has been widely validated. From a technical evolution perspective, MoE architecture is still being continuously optimized, with active exploration in areas including expert load balancing strategies (preventing some experts from being overused while others sit idle), shared expert design (all inputs pass through certain universal experts to ensure baseline capability), and fine-grained expert splitting (dividing traditional large experts into more small experts to improve routing flexibility). The DeepSeekMoE architecture adopted by DeepSeek-V3 introduced shared experts and fine-grained routing design, significantly improving MoE model training stability and final performance.

Model Specification Choices Are Becoming More Refined

From 7B, 14B to 30B, 72B, and various activated parameter configurations, teams need to repeatedly weigh capability, cost, and deployment difficulty. Abandoning a certain intermediate specification precisely reflects judgment about users' real needs—avoiding overwhelming developers with too many similar options.

Community-Official Information Interaction Is Growing Tighter

The fact that a brief remark from a developer can trigger large-scale discussion reflects the extremely high expectations the open-source community holds for leading teams like Qwen. This close interaction itself is a sign of a healthy open-source ecosystem. Unlike closed-source models (such as OpenAI's GPT series, Anthropic's Claude series), open-source model teams have a more direct feedback loop with their communities: community needs and feedback directly influence R&D priorities, while any team activity gets immediately captured and discussed by the community. This symbiotic relationship is a crucial driver of rapid open-source LLM evolution.

A Rational Take on Official Hints: What Developers Should Focus On

It's important to note that this message is still an informal developer statement circulating in the community, with no official roadmap confirmation yet. Until the Qwen team releases an official announcement, all claims about "122B" or "canceled plans" remain speculation.

For regular users and developers, rather than obsessing over whether a specific specification will be released, it's better to focus on a model's actual capabilities and fit for your use case. If the existing Qwen3 30B-A3B already meets your needs, then whether a 35B version exists has limited impact; and if a more powerful new model does arrive, you can make your choice based on real-world testing at that time. When actually selecting a model, while benchmark scores (such as MMLU, HumanEval, GSM8K, etc.) are certainly important, you should pay more attention to actual performance in your specific use case—including response quality, inference speed, context window length, and compatibility with your existing toolchain.

Conclusion

"Don't wait for 35B-A3B"—this statement could herald a more exciting new model, or it could simply be the team's pragmatic pruning of its product line. It's like a cliffhanger, leaving the community curious about Qwen's next move.

In today's rapidly evolving open-source LLM landscape, any specification adjustment can ripple through the entire ecosystem. Let's stay tuned and wait for the Qwen team's official answer—whether that's a stronger flagship model or a strategic decision to focus on what matters most.