Guide on Hyperparameter Tuning#
Achieving Peak Throughput#
Achieving a large batch size is the most important thing for attaining high throughput.
When the server is running at full load, look for the following in the log:
Decode batch. #running-req: 233, #token: 370959, token usage: 0.82, gen throughput (token/s): 4594.01, #queue-req: 317
Tune Your Request Submission Speed#
#queue-req
indicates the number of requests in the queue. If you frequently see #queue-req == 0
, it suggests you are bottlenecked by the request submission speed.
A healthy range for #queue-req
is 50 - 500
.
On the other hand, do not make #queue-req
too large because it will also increase the scheduling overhead on the server, especially when using the default longest-prefix-match schedule policy (--schedule-policy lpm
).
Tune --schedule-conservativeness
#
token usage
indicates the KV cache memory utilization of the server. token usage > 0.9
means good utilization.
If you frequently see token usage < 0.9
and #queue-req > 0
, it means the server is too conservative about taking in new requests. You can decrease --schedule-conservativeness
to a value like 0.3.
The case of server being too conservative can happen when users send many requests with a large max_new_tokens
but the requests stop very early due to EOS or stop strings.
On the other hand, if you see token usage
very high and you frequently see warnings like
decode out of memory happened, #retracted_reqs: 1, #new_token_ratio: 0.9998 -> 1.0000
, you can increase --schedule-conservativeness
to a value like 1.3.
If you see decode out of memory happened
occasionally but not frequently, it is okay.
Tune --dp-size
and --tp-size
#
Data parallelism is better for throughput. When there is enough GPU memory, always favor data parallelism for throughput.
Avoid out-of-memory by Tuning --chunked-prefill-size
, --mem-fraction-static
, --max-running-requests
#
If you see out of memory (OOM) errors, you can try to tune the following parameters.
If OOM happens during prefill, try to decrease
--chunked-prefill-size
to4096
or2048
.If OOM happens during decoding, try to decrease
--max-running-requests
.You can also try to decrease
--mem-fraction-static
, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.
Try Advanced Options#
To enable torch.compile acceleration, add
--enable-torch-compile
. It accelerates small models on small batch sizes. This does not work for FP8 currently.
Tune --schedule-policy
#
If the workload has many shared prefixes, use the default --schedule-policy lpm
. lpm
stands for longest prefix match.
When you have no shared prefixes at all or you always send the requests with the shared prefixes together,
you can try --schedule-policy fcfs
. fcfs
stands for first come first serve. fcfs
has a lower scheduling overhead.