Unverified50% confidenceBenchmarkExact time
When running IQ3_XXS quantized models with llama.cpp, the output speed is only 15 tok/s with prompt processing at 100–120 tok/s, while a self-developed engine brings the same quantization up to approximately 65 tok/s output and 430 tok/s prompt processing, representing close to a 4x performance improvement.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/24/2026
First Seen
Valid until: 12/23/2026
Sources
Related Entities
Related Claims
UnverifiedThe output speed of traditional large models is typically between 50 and 150 tokens per second, while high-performance models such as Claude and GPT-4o can reach 200 to 400 tokens per second after optimization.72% similarUnverifiedAt approximately 52 concurrent connections, the system is essentially 100% saturated, generating a maximum of approximately 704 tokens per second, but with latency spiking to 64 seconds.70% similarUnverifiedAfter V3 introduced KV caching, the time to generate 100 tokens from a 512-token prompt dropped from 278 seconds to 28 seconds, nearly a 10× speedup.69% similarUnverifiedUsing TaoMate's 3-step ref2va turbo LoRA, generating a 9.4-second clip on an RTX 3090 takes approximately 4–5 minutes, a significant speedup compared to 12.5 minutes with 8 steps.69% similar
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