ISTA Releases DeepSeek-V4.1-Flash-1017-GSQ-RCO Coder: A 763B Model with 1 Bit per Parameter
By Mr.Xu Community Post
Published:
Summary:ISTA Deep Algorithms and Systems Lab has released the Coder build of DeepSeek-V4.1-Flash-1017, compressed using GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization) techniques, achieving 1 bit per parameter. This model retains 98.7% of LiveCodeBench performance and 94.8% of SWE-bench performance while significantly reducing storage requirements by pruning half of the experts. This release is particularly suited for AI applications in resource-constrained environments.
Technical Mechanism Analysis
DeepSeek-V4.1-Flash-1017 is a sparse mixture-of-experts model with 384 routed experts per layer, of which 6 are active per token. The total parameter count is 763.2B, with 551.9B parameters in the Transformer module. The Coder version of this model achieves high compression through the following techniques:
- GSQ (Gumbel-Softmax Quantization): A post-training scalar quantization technique that jointly learns grid assignments and group scales, bridging the gap between scalar and vector quantization at low bit-widths while maintaining deployability in standard GGUF formats.
- RCO (Riemannian Constrained Optimization): Enforces exact budgets by performing gradient descent on the task loss without per-constraint tuning. In this application, RCO selects which experts to keep, optimizing the KL divergence to the unpruned model while maintaining a strict budget per layer.
Engineering Trade-offs and Performance
- Storage Optimization: After pruning half of the experts, the model retains 192 experts per layer, reducing the Transformer weights from 138.2 GB to 72.1 GB.
- Quantization Efficiency: The model achieves 1 bit per parameter, with the 280.1B parameters averaging 2.06 bits. The shared Engram file and vision projector files are the same as in the unpruned version, requiring only one download.
- Performance Retention: On LiveCodeBench v6, the model scores 88.57 compared to the baseline model's 89.71, retaining 98.7% of the performance. On SWE-bench Verified, it scores 77.00 compared to the baseline model's 81.20, retaining 94.8% of the performance.
Developer Implementation and Deployment Recommendations
- Use Cases: This model is suitable for AI applications that require efficient inference and low storage requirements, such as edge computing devices or resource-constrained environments.
- Deployment Recommendations: The GGUF files can run on llama.cpp b11870 or newer versions and in Strata.
- Feedback and Improvement: As this is an experimental release, ISTA welcomes feedback from developers, particularly on capabilities not represented in the calibration regime. ISTA also welcomes requests for quantizing or pruning other models.
Conclusion
The DeepSeek-V4.1-Flash-1017-GSQ-RCO Coder, through innovative quantization and pruning techniques, achieves significant storage optimization while maintaining high performance, opening new possibilities for the widespread application of AI models.
— END —Source: Reddit r/LocalLLaMA (2026-11-02)
Tags: #DeepSeek #Quantized Model #Sparse Expert Model #GSQ #RCO
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