Unsloth Releases Quantized DeepSeek V4 Flash 0731 for Local Deployment
Unsloth released GGUF dynamic quantizations of DeepSeek V4 Flash 0731 from July 31 to August 2, enabling local deployment. The model is based on the deepseekv4 architecture and is MIT-licensed. This release significantly lowers the barrier for local deployment, sparking community discussion on the trade-off between quantization precision and performance.
Confirmed
- Unsloth published quantized versions of DeepSeek-V4-Flash-0731 on Hugging Face, with multiple dynamic quantization (UD) options ranging from 83GB to 162GB.
- The 162GB UD-Q8KXL version is fully lossless, while the smallest 83GB IQ1S version retains 73% performance; the 97GB version is also a viable option.
- Users can run the model via Unsloth or llama.cpp. Hardware requirements: 168GB RAM for lossless 4-bit quantization, 110GB RAM for 3-bit quantization.
Unconfirmed
- Developer danielhanchen addressed community questions about quantization naming and precision differences, but the specific details and clarifications are not fully presented in the materials.
Why it matters
- This release enables local deployment of DeepSeek V4 Flash 0731 on consumer hardware, offering flexible options for developers with different VRAM environments, which is significant for AI developers and researchers.
2026-07-31 ~ 2026-08-02 · 6 related posts
- Episode 1: DeepSeek-V4-Flash Architecture Leaked with Million-Token Context(2026-07-31, 3 posts)
- Episode 2: Unsloth Releases Quantized DeepSeek V4 Flash 0731 for Local Deployment(2026-07-31, 6 posts)
- Episode 3: DeepSeek on Huawei Ascend Beats OpenAI in Inference Profitability(2026-08-01, 2 posts)
- Episode 4: DeepSeek-V4-Flash Excels in Frontend Coding with Unmatched Cost-Performance(2026-08-01, 3 posts)
- Episode 5: DeepSeek V4 Preview: Flash to Introduce Four-Level Reasoning Effort(2026-08-01, 2 posts)
- Episode 6: DeepSeek Drastically Reduces Training Compute Costs Across Models(2026-08-01, 2 posts)
- Episode 7: DeepSeek V4-Flash API Public Beta Launches with Major Agent Upgrades(2026-08-01, 5 posts)
- Episode 8: DeepSeek V4-Flash Costs 105x Less, But Stability and Benchmark Overfitting Questioned(2026-08-01, 17 posts)
- Episode 9: DeepSeek V4 Flash Sparks a Wave of Local Deployment Tests(2026-08-01, 26 posts)
- Episode 10: DeepSeek V4 Flash Quantization Tests Show Major Speed Gains Without Quality Loss(2026-08-04, 2 posts)
- Episode 11: Single RTX 5090 Runs DeepSeek 1M Context(2026-08-04, 2 posts)
- Episode 12: DeepSeek-V4-Flash Tops Cost-Efficiency with Ultra-Low Running Costs(2026-08-05, 4 posts)
- Episode 13: DeepSeek V4 Flash Benchmarks Leak, Outperforming Pro(2026-08-05, 3 posts)
- Episode 14: DeepSeek V4 Flash Tops ARC-AGI Cost-Performance, Costing a Quarter of GPT-5.6(2026-08-08, 9 posts)
- Episode 15: DeepSeek V4 Flash Passes 22 Coding Tests on Dual DGX Spark Cluster(2026-08-10, 2 posts)
Primary sources
- [source] unsloth Releases Quantized DeepSeek-V4-Flash-0731 Model — unsloth · 2026-07-31
- Unsloth releases GGUF quantized formats for DeepSeek V4 0731 — BlackBeardAI · 2026-07-31
- [source] Unsloth Enables Local Lossless Deployment of DeepSeek V4-Flash on 110GB RAM — gnukeith · 2026-08-01
- [source] Unsloth Releases Dynamic Quants for DeepSeek V4 Flash 0731 — danielhanchen · 2026-08-01
- Running DeepSeek V4 Flash Locally: Demystifying Quantization Naming and Precision — TheZachMueller · 2026-08-01
- Unsloth Releases DeepSeek V4 Quants: Runs in 83GB VRAM — QuixiAI · 2026-08-02