Evaluating Qwen 3.5 122B, Coder Next, and Nvidia 3 Super for Local Coding

This review assesses three large language models—Qwen 3.5 122B A10B, Qwen3 Coder Next, and Nvidia 3 Super—for local coding on a dual RTX A6000 setup with 96GB VRAM and NVLink, focusing on long-context performance.

The Answer Up Front

For users with a dual RTX A6000 setup and 96GB of VRAM linked via NVLink, seeking a local LLM for long-context, messy coding of large repositories, Qwen3 Coder Next is the most pragmatic daily driver. Its specialized training for coding tasks generally outperforms larger, more general models in code-specific scenarios. While Qwen 3.5 122B A10B offers a broader knowledge base, its utility for coding tasks, particularly with extensive context, may not justify the increased resource demands over a purpose-built coder model. Nvidia 3 Super is a newer entrant that requires specific benchmarking to confirm its coding prowess against established options.

Methodology

This v0 review draws on the user 'EggDroppedSoup''s published claims and queries on Reddit, accessed on 2026-05-27. Independent benchmarks are pending. Update cadence: re-tested when claims diverge from observed behavior. This review covers the founder's own claims and the general public understanding of the models mentioned: Qwen 3.5 122B A10B, Qwen3 Coder Next, and Nvidia 3 Super. The user's hardware setup (2 RTX A6000 GPUs with 96GB VRAM and NVLink) is a critical component of the assessment, as it dictates the feasible model sizes and context lengths. This review does not cover independent performance benchmarks, long-term workflow integration, or edge-case handling for each model. The user's experience with Qwen 3.6 27B (Q8) and 35 A3B (Q4) and their observation that "MoE qwen with MTP is lightning fast with opencode at finding bug" provides a baseline for expected performance characteristics, particularly regarding quantization and mixture-of-experts architectures.

What It Does

Qwen 3.5 122B A10B

This model is a large-scale general-purpose language model from Alibaba Cloud's Qwen series. The user 'EggDroppedSoup' notes that some sources claim the 122B variant is superior due to its extensive knowledge. With 96GB of VRAM and NVLink, running a quantized version of this model, potentially at Q4 or Q8, is feasible. Its strength lies in its broad understanding and ability to handle diverse tasks, which could translate to better contextual understanding for very large, complex codebases, even if not explicitly fine-tuned for coding.

Qwen3 Coder Next

Qwen3 Coder Next is a specialized iteration of the Qwen series, specifically fine-tuned for coding tasks. The user reports that some community members consider it their "go to daily" driver. This specialization implies a stronger grasp of programming languages, common coding patterns, and debugging scenarios compared to generalist models. For tasks like code completion, refactoring, and understanding complex project structures, a coding-specific model often provides more relevant and accurate outputs, even if its general knowledge base is smaller than a 122B variant.

Nvidia 3 Super

The signal mentions "Nvidia 3 Super" as another model under consideration. Details on this specific model's architecture, training data, and coding capabilities are not widely public at the time of this review. Nvidia has released various models and tools for AI development, but a distinct "Nvidia 3 Super" with a clear coding focus is not a prominent, established name in the same vein as the Qwen series for local LLMs. Without further information or public benchmarks, its suitability for long-context coding on the specified hardware remains speculative.

What's Interesting / What's Not

The user's hardware setup, dual RTX A6000 with 96GB VRAM and NVLink, is a significant asset, enabling the use of much larger models and longer context windows than typical consumer setups. This capacity makes the discussion of 100B+ parameter models viable for local inference. The user's observation about "byteshape quant is insane" for Qwen 35 A3B (Q4) highlights the critical role of advanced quantization techniques in making these large models practical for local deployment, especially when balancing performance and VRAM usage. The trade-off between a generalist model's broad knowledge (Qwen 3.5 122B A10B) and a specialist's focused expertise (Qwen3 Coder Next) is the core tension here. For coding, especially "messy coding of large repos," the depth of understanding provided by a coding-specific fine-tune often outweighs the breadth of a generalist model. Generalist models may struggle with the nuances and specific syntax of code, leading to less useful suggestions or more hallucinations in a coding context. The mention of "Nvidia 3 Super" is less interesting without specific public details on its coding performance or architecture; it could be a general model or an internal project, making it difficult to assess its competitive edge.

Pricing

Qwen 3.5 122B A10B, Qwen3 Coder Next, and other models in the Qwen series are generally open-source and available for free download and local inference. The primary cost associated with their use is the underlying hardware (e.g., the 2x RTX A6000 GPUs) and the electricity consumed for inference. This pricing snapshot is accurate as of May 2026.

Verdict

For 'EggDroppedSoup''s specific use case of long-context, messy coding of large repositories on a dual RTX A6000 setup with 96GB VRAM and NVLink, Qwen3 Coder Next is the recommended daily driver. Its explicit fine-tuning for coding tasks makes it more likely to provide accurate and relevant suggestions for code completion, refactoring, and bug identification. While Qwen 3.5 122B A10B offers a larger parameter count and broader knowledge, this does not automatically translate to superior performance in highly specialized coding contexts. The overhead of a larger generalist model for coding-specific tasks may not yield proportional benefits. Nvidia 3 Super remains an unknown quantity without further public details or benchmarks.

What We'd Test Next

To provide a definitive recommendation, we would establish a benchmark suite focused on long-context coding scenarios. This would involve testing each model's ability to understand and generate code within large repositories (e.g., 50k-100k lines of code), specifically evaluating performance on tasks like cross-file refactoring, identifying subtle bugs across multiple modules, and generating new features consistent with existing large codebases. We would measure inference speed, VRAM utilization with NVLink enabled, and the quality of generated code for varying context window sizes. Quantization levels (e.g., Q4, Q8) for each model would also be systematically compared to find the optimal balance between performance and output quality on the specified hardware.

The investor read

The user's query highlights a growing segment of tooling spend: high-end local inference hardware for specialized AI tasks. The ability to run 100B+ parameter models locally, particularly for coding, indicates a maturation of the local LLM ecosystem and a potential shift in developer workflow. Companies building optimized quantization techniques, efficient inference engines, or highly specialized fine-tuned models for specific domains (like coding) are well-positioned. The trade-off between generalist and specialist models for local deployment is a key market dynamic. An investable company in this space would demonstrate superior performance benchmarks on common developer tasks, robust support for diverse hardware configurations, and a clear path to integrate with existing developer toolchains. The market is moving beyond simple chat models to highly functional, domain-specific local agents.

Pull quote: “For users with a dual RTX A6000 setup and 96GB of VRAM linked via NVLink, seeking a local LLM for long-context, messy coding of large repositories, Qwen3 Coder Next is the most pragmatic daily driver.”