Unverified60% confidenceTradeoffExact time
Q4_K_M is the most mainstream balanced quantization choice, with perplexity loss typically below 1%; Q2_K can compress a 70B model to approximately 26GB but with a noticeable quality degradation.
2
Sources
60%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Ollama Raises $65M Series B: 85% of Fortune 500 Have Already Deployed Local LLMs
redditr/ollama7/10/2026
Related Claims
UnverifiedQ2_K quantization compresses model weights to an average of approximately 2.5 bits, reducing VRAM usage to roughly one-sixth of the original, but causes significant degradation of model capability, jokingly referred to in the industry as a lobotomy.80% similarUnverifiedThe higher the quantization level (e.g., Q4), the less VRAM is consumed, but generation quality will decrease accordingly.78% similarUnverifiedQ8_0 quantization compresses model weights from 32-bit floating-point numbers to 8-bit integers, with precision loss typically within 1% compared to FP32.75% similarUnverifiedPrecision loss comparison by quantization level: Q5 is nearly lossless, Q4 has approximately 2%–5% loss, Q3 has approximately 8%–15% loss, and Q2 has approximately 30%–50% loss.74% similarUnverifiedA 70B parameter model after Q4 quantization often still has a lower perplexity than a 13B model under Q8 or even FP16.71% similar
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