update benchmarks

README.md

@@ -64,17 +64,17 @@ Visit our [documentation](./docs/) to get started:
 - [Prefill-decoding Disaggregation](./docs/Prefill-decoding_Disaggregation.md)
 
 ## Performance
-We evaluate the performance of the KV-cache-aware load-balancing scheduler and migration mechanism of Llumnix with 16 Llama2-7B/Qwen1.5-7B instances, each using an A10 GPU (24GB).
+We evaluate the performance of the KV-cache-aware load-balancing scheduler and migration mechanism of Llumnix with 16 Qwen2.5-7B instances (each using an A10-24GB GPU) and 16 Llama2-13B instances (each using an A800-80GB GPU).
 
 We use Poisson distributions with different request rates to generate request arrivals. For the input/output lengths of requests, we use ShareGPT dataset.
 
 <div align=center>
-<img src="./docs/performance.png" align="center" width=80%/>
+<img src="./docs/v0.1.0_benchmark.png" align="center" width=80%/>
 </div>
 
-With the KV-cache-aware load-balancing scheduler, Llumnix outperforms a simple load balancing scheduler based on queue sizes in TTFT (prefill) by up to 1.8x and 7.7x for mean and P99, and 1.4x for P99 TBT (decode).
+Llumnix outperforms a simple round-robin scheduler in TTFT (prefill) by up to 6.4x and 12.1x for mean and P99, and 12% for P99 TBT (decode). Llumnix also shows significantly shorter average preemption stalls (by two orders of magnitude).
 
-With migration mechanism, Llumnix maintains lower preemption stalls, further outperformers load-balance scheduler in TTFT by up to 1.7x and 3.3x for mean and P99, and 1.3x for P99 TBT.
+With the KV-cache-aware load-balancing scheduler and the migration mechanism, Llumnix also outperforms a simple load balancing scheduler based on queue sizes in TTFT (prefill) by up to 4.6x and 9.1x for mean and P99, and 15% for P99 TBT (decode).
 
 ## Roadmap