Is 1-bit and TurboQuant the future of OSS? A simulation for Qwen3.5 models.

Reddit r/LocalLLaMA / 4/2/2026

💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsModels & Research

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Key Points

The post simulates what Qwen3.5 model variants could look like if weights were stored in 1-bit format and KV cache memory were optimized with TurboQuant.
It reports substantial hypothetical memory reductions versus current Q4_K_M weights and a 256K KV cache, with total memory usage dropping sharply across multiple model sizes.
Example results show the largest model (Qwen3.5-122B) potentially shrinking from about 156GB total (74.99GB weights + 81.43GB KV cache) to about 18.20GB under the combined 1-bit + TurboQuant scenario.
Smaller variants (e.g., 4B and 2B) are also projected to fall to roughly 1.99GB and 0.82GB respectively, implying materially easier deployment for open-source setups.
The author frames the concept as a potential “revolution” for OSS by making larger models feasible within far tighter hardware constraints, though the numbers are explicitly hypothetical simulations rather than demonstrated releases.

Simulation what the Qwen3.5 model family would look like using 1-bit technology and TurboQuant. The table below shows the results, this would be a revolution:

Model	Parameters	Q4_K_M File (Current)	KV Cache (256K) (Current)	Hypothetical 1-bit Weights	KV Cache 256K with TurboQuant	Hypothetical Total Memory Usage
Qwen3.5-122B-A10B	122B total / 10B active	74.99 GB	81.43 GB	17.13 GB	1.07 GB	18.20 GB
Qwen3.5-35B-A3B	35B total / 3B active	21.40 GB	26.77 GB	4.91 GB	0.89 GB	5.81 GB
Qwen3.5-27B	27B	17.13 GB	34.31 GB	3.79 GB	2.86 GB	6.65 GB
Qwen3.5-9B	9B	5.89 GB	14.48 GB	1.26 GB	1.43 GB	2.69 GB
Qwen3.5-4B	4B	2.87 GB	11.46 GB	0.56 GB	1.43 GB	1.99 GB
Qwen3.5-2B	2B	1.33 GB	4.55 GB	0.28 GB	0.54 GB	0.82 GB