Open-source memory wars are heating up, and LongCat-Flash-Thinking is at the center. The approach is a case study in squeezing bigger contexts onto lean hardware without blowing RAM. It’s pitched as SOTA for logic/math/coding/agent tasks, with claims of 64.5% fewer tokens to top-tier accuracy on AIME25 using native tools and a 3x Async RL speedup, signaling a broader move toward efficiency [1].
Lean, mean reasoning — LongCat-Flash-Thinking sits among open models aiming to trim footprints while keeping context lengths practical. Proponents point to a roughly 350–400 GB memory footprint on aggressive setups and note it’s smaller than DeepSeek V3, which others run on weaker hardware with low-bitrate quantization and MOE tricks [1]. They also cite milestones like Kimi K2 (1026B total, 32B active) as proof that clever quantization and sparse architecture can bend hardware ceilings [1].
Open-source stacks race on efficiency — Other stacks like DeepSeek, Qwen, and GLM are framed as contenders in the quest to stretch context length without requiring cloud-scale hardware. The thread highlights how this movement isn’t just about speed, but enabling longer conversations and more capable reasoning on more modest rigs [1].
KV-cache quantization surprises — In a local benchmark run on a llama.cpp server with Qwen3-30B-A3B-Instruct-2507 (131 samples from LongBench-v2, 16k–51k tokens), a counter-intuitive result emerged: the best setup, k-f16v-q50, scored 16.79% vs the full-precision baseline at 13.74% [2]. The test underscores how local quantization can flip expectations in long-context scenarios. The work also notes Unsloth Dynamic 2.0 Q4KXL as a recommended local-inference quant type in this space [2].
As memory gets cheaper to manage, the race isn’t about raw model size—it’s about what open models can do with bigger contexts on affordable hardware. Expect more KV-cache tricks and MOE-oriented designs to surface soon. [1][2]
References
LongCat-Flash-Thinking
Promotes LongCat-Flash-Thinking as fast, efficient, open-source model; discusses quantization, memory needs, and comparisons to DeepSeek, Qwen, GLM.
View sourceGetting counter-intuitive results with local KV Cache Quantization Benchmark - am I doing something wrong?
Discusses long-context KV-cache quantization benchmarks (16k–51k tokens) across models, debating FP16 vs quantized caches and statistical significance and methodology concerns.
View source