Unverified50% confidenceBenchmarkExact time
The Gemma open-source model achieved 2.85x end-to-end inference acceleration on A100 via vLLM, with throughput increasing from about 37,900 tokens/second to over 108,000 tokens/second, while maintaining 100% token consistency and zero quality degradation.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
UnverifiedReal-world testing with the 284-billion-parameter DeepSeek V4 Flash shows token generation rising from 37 to 53 tokens/s (approximately 1.5x), and Prompt Processing jumping from 483 to 1,485 tokens/s (approximately three times).69% similarUnverifiedOpenTPU 运行 LFM 2.5 230M 模型可达到约 59 tokens/秒，在 4-bit 量化下提升到 85.8 tokens/秒68% similarUnverifiedAn LLM inference project called Nori claims to have achieved a processing throughput of over 1 million tokens per second (1M tok/s).68% similarUnverifiedOn the same machine and model, the decoding speed difference between low power mode and high power mode is 1.4 to 3.8 times; for example, running Qwen 3.5 35B on Ollama gives 51.9 tokens/s in low power and 107.1 tokens/s in high power.67% similarUnverifiedDeveloper Primitive reported: 91.2 tokens/s with speculative decoding off, reaching 142.6 tokens/s after enabling 3 speculative tokens, approximately a 56% speedup under the same configuration.67% similar
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