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// 1h agoBENCHMARK RESULT
DeepSeek SAGE-EXL3 Challenges GGUF, GSQ-RCO
This 1.55-bpw EXL3 quantization of DeepSeek-V4.1-Flash-1017 targets a 117.2GB backbone and competes with Unsloth GGUF and GSQ-RCO builds. Early results lead the eight-quant HMMT comparison at 70.27% and outperform most rivals on AIME, while retries and 262K-context testing continue.
// ANALYSIS
Aggressive MoE quantization is becoming a credible way to run frontier-scale models locally, but these results remain an early, hardware-specific comparison rather than proof of universal superiority.
- –HMMT leads all eight tested variants; AIME leads five of eight, while IFEval leads only one.
- –EXL3’s GPU-focused efficiency complements emerging single-DGX-Spark runtimes, including native ExLlamaV3 recipes. [Spark recipe](https://github.com/vcruz305/DeepSeek-V4.1-Flash-EXL3-DGX-Spark-recipe)
- –GGUF remains more broadly compatible across CPU, Metal, and mixed-hardware setups, so raw benchmark wins do not settle the deployment trade-off.
- –The 262K one-Spark target is the most meaningful developer milestone: long-context usability matters more than isolated benchmark victories.
- –Retried scores, prompt parity, sampling settings, and full-model memory requirements should be published before treating the leaderboard claims as conclusive.
// TAGS
dsv4-1-flash-1017-sage-exl3-1-55bpwllmopen-weightsquantizationbenchmarkinferencelocal-first
DISCOVERED
1h ago
2026-11-02
PUBLISHED
1h ago
2026-11-02
RELEVANCE
9/ 10
AUTHOR
ViC305