Unverified50% confidenceBenchmarkExact time
On an M2 Max, using Llama-3.1-8B-Instruct (Q4_K_M quantization), Magnitude's throughput is 38.7 tok/s, and Ollama's is 18.2 tok/s.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/17/2026
First Seen
Valid until: 1/15/2027
Sources
Related Entities
Related Claims
UnverifiedQ4_K_M量化文件大小17.77GB，pp2048速度96.8 tok/s，tg128速度11.4 tok/s75% similarUnverifiedOrnith 1.5 requires approximately 23 GB of VRAM for the recommended Q4_K_M quantization with the full package, and it is advised to prepare 27 GB or more; Qwen 3.8 at the same Q4_K_M tier requires only 16 to 19 GB.73% similarUnverifiedQ3_K_M compresses each weight to approximately 3 bits, reducing the weight size of a 27B model to approximately 12–13 GB.72% similarUnverifiedLiquidAI's LFM2.5-24B-A2B uses sparse MoE to activate only 2B per token out of 24B total parameters, extends context from 32K to 131K, runs at 184 tok/s on an Apple M5 Max, and reaches aggregate throughput of up to 21K tok/s on a single NVIDIA B300.72% similarUnverifiedUD-Q4_K_XL量化版本体积155GB，Top-1精度为96%70% similar
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