Llama 4
1. Model Introduction
Llama 4 is Meta's latest generation of open-source LLM model with industry-leading performance.
SGLang has supported Llama 4 Scout (109B) and Llama 4 Maverick (400B) since v0.4.5.
Ongoing optimizations are tracked in the Roadmap.
This generation delivers comprehensive upgrades across the board:
The highly capable Llama 4 Maverick with 17B active parameters out of ~400B total, with 128 experts. The efficient Llama 4 Scout also has 17B active parameters out of ~109B total, using just 16 experts. Both models leverage early fusion for native multimodality, enabling them to process text and image inputs. Maverick and Scout are both trained on up to 40 trillion tokens on data encompassing 200 languages (with specific fine-tuning support for 12 languages including Arabic, Spanish, German, and Hindi).
For more details, please refer to the official llama4 Repository:https://www.llama.com/models/llama-4/
2. SGLang Installation
SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
Please refer to the official SGLang installation guide for installation instructions.
3. Model Deployment
This section provides a progressive guide from quick deployment to performance optimization, suitable for users at different levels.
3.1 Basic Configuration
Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, model variant, deployment strategy, and thinking capabilities.
Hardware Platform
Quantization
Tool Call Parser
Speculative Decoding (EAGLE3)
Host
Port
Run this Command:
python -m sglang.launch_server \ --model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \ --tp 8 \ --enable-multimodal \ --context-length 65536 \ --dtype bfloat16 \ --trust-remote-code \ --host 0.0.0.0 \ --port 8000
Hardware Platform
Host
Port
Run this Command:
python -m sglang.launch_server \ --model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \ --tp 8 \ --enable-multimodal \ --context-length 65536 \ --dtype bfloat16 \ --trust-remote-code \ --host 0.0.0.0 \ --port 8000
4. Model Invocation
4.1 Basic Usage
For basic API usage and request examples, please refer to:
4.2 Advanced Usage
4.2.1 Launch the docker
docker pull lmsysorg/sglang:v0.5.9-rocm720-mi30x
docker run -d -it --ipc=host --network=host --privileged \
--cap-add=CAP_SYS_ADMIN \
--device=/dev/kfd --device=/dev/dri --device=/dev/mem \
--group-add video --cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-v /:/work \
-e SHELL=/bin/bash \
--name Llama4 \
lmsysorg/sglang:v0.5.9-rocm720-mi30x \
/bin/bash
4.2.2 Launch the server
Llama-4-Scout
8-GPU deployment command:
sglang serve \
--model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \
--tp 8 \
--context-length 1000000 \
--trust-remote-code
Llama-4-Maverick
8-GPU deployment command:
sglang serve \
--model-path meta-llama/Llama-4-Maverick-17B-128E-Instruct \
--tp 8 \
--context-length 1000000 \
--trust-remote-code
5. Benchmark
5.1 Speed Benchmark
Test Environment:
Hardware: AMD MI300x GPU
Model: Llama-4-Scout
Tensor Parallelism: 8
sglang version: 0.5.9
- Model Deployment
sglang serve \
--model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \
--tp 8 \
--context-length 1000000 \
--trust-remote-code
5.1.1 Low Concurrency (Latency-Optimized)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 74.62
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 4220
Total generated tokens (retokenized): 4211
Request throughput (req/s): 0.14
Input token throughput (tok/s): 82.88
Output token throughput (tok/s): 57.42
Peak output token throughput (tok/s): 146.00
Peak concurrent requests: 2
Total token throughput (tok/s): 140.20
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 7459.48
Median E2E Latency (ms): 4489.77
---------------Time to First Token----------------
Mean TTFT (ms): 4246.98
Median TTFT (ms): 68.57
P99 TTFT (ms): 48091.05
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 7.49
Median TPOT (ms): 7.40
P99 TPOT (ms): 7.40
---------------Inter-Token Latency----------------
Mean ITL (ms): 7.49
Median ITL (ms): 7.49
P95 ITL (ms): 7.47
P99 ITL (ms): 7.52
Max ITL (ms): 10.44
==================================================
5.1.2 Medium Concurrency (Balanced)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 80 \
--max-concurrency 16 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 16
Successful requests: 80
Benchmark duration (s): 45.41
Total input tokens: 49668
Total input text tokens: 49668
Total input vision tokens: 0
Total generated tokens: 40805
Total generated tokens (retokenized): 40516
Request throughput (req/s): 2.26
Input token throughput (tok/s): 1120.46
Output token throughput (tok/s): 1152.47
Peak output token throughput (tok/s): 1520.00
Peak concurrent requests: 21
Total token throughput (tok/s): 2272.84
Concurrency: 14.76
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 6089.22
Median E2E Latency (ms): 6568.80
---------------Time to First Token----------------
Mean TTFT (ms): 124.44
Median TTFT (ms): 87.42
P99 TTFT (ms): 268.72
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 11.88
Median TPOT (ms): 12.00
P99 TPOT (ms): 15.49
---------------Inter-Token Latency----------------
Mean ITL (ms): 11.72
Median ITL (ms): 10.54
P95 ITL (ms): 11.22
P99 ITL (ms): 67.88
Max ITL (ms): 74.05
==================================================
5.1.3 High Concurrency (Throughput-Optimized)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 500 \
--max-concurrency 100 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 500
Benchmark duration (s): 85.84
Total input tokens: 249841
Total input text tokens: 249841
Total input vision tokens: 0
Total generated tokens: 252662
Total generated tokens (retokenized): 250498
Request throughput (req/s): 5.84
Input token throughput (tok/s): 2910.84
Output token throughput (tok/s): 2944.82
Peak output token throughput (tok/s): 4100.00
Peak concurrent requests: 110
Total token throughput (tok/s): 5854.65
Concurrency: 92.24
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 15844.00
Median E2E Latency (ms): 15262.56
---------------Time to First Token----------------
Mean TTFT (ms): 204.46
Median TTFT (ms): 129.96
P99 TTFT (ms): 528.54
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 41.56
Median TPOT (ms): 42.90
P99 TPOT (ms): 47.48
---------------Inter-Token Latency----------------
Mean ITL (ms): 40.99
Median ITL (ms): 24.46
P95 ITL (ms): 84.46
P99 ITL (ms): 87.64
Max ITL (ms): 226.06
==================================================
5.2 Speed Benchmark
Test Environment:
Hardware: AMD MI300x GPU
Model: Llama-4-Maverick
Tensor Parallelism: 8
sglang version: 0.5.9
- Model Deployment
sglang serve \
--model-path meta-llama/Llama-4-Maverick-17B-128E-Instruct \
--tp 8 \
--context-length 1000000 \
--trust-remote-code
5.2.1 Low Concurrency (Latency-Optimized)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Maverick-17B-128E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 68.08
Total input tokens: 6101
Total input text tokens: 6101
Total input vision tokens: 0
Total generated tokens: 4220
Total generated tokens (retokenized): 4202
Request throughput (req/s): 0.15
Input token throughput (tok/s): 89.62
Output token throughput (tok/s): 61.99
Peak output token throughput (tok/s): 168.00
Peak concurrent requests: 2
Total token throughput (tok/s): 151.61
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 6805.62
Median E2E Latency (ms): 2733.91
---------------Time to First Token----------------
Mean TTFT (ms): 4296.56
Median TTFT (ms): 57.45
P99 TTFT (ms): 38633.95
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 5.95
Median TPOT (ms): 5.96
P99 TPOT (ms): 5.97
---------------Inter-Token Latency----------------
Mean ITL (ms): 5.96
Median ITL (ms): 5.96
P95 ITL (ms): 6.02
P99 ITL (ms): 6.08
Max ITL (ms): 7.02
==================================================
5.2.2 Medium Concurrency (Balanced)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Maverick-17B-128E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 80 \
--max-concurrency 16 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 16
Successful requests: 80
Benchmark duration (s): 30.72
Total input tokens: 39668
Total input text tokens: 39668
Total input vision tokens: 0
Total generated tokens: 40805
Total generated tokens (retokenized): 40923
Request throughput (req/s): 2.60
Input token throughput (tok/s): 1291.39
Output token throughput (tok/s): 1328.41
Peak output token throughput (tok/s): 1760.00
Peak concurrent requests: 22
Total token throughput (tok/s): 2619.80
Concurrency: 13.92
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 5345.15
Median E2E Latency (ms): 5679.73
---------------Time to First Token----------------
Mean TTFT (ms): 259.30
Median TTFT (ms): 72.60
P99 TTFT (ms): 1063.45
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 10.53
Median TPOT (ms): 10.22
P99 TPOT (ms): 20.27
---------------Inter-Token Latency----------------
Mean ITL (ms): 9.99
Median ITL (ms): 9.10
P95 ITL (ms): 9.87
P99 ITL (ms): 55.62
Max ITL (ms): 868.54
==================================================
5.2.3 High Concurrency (Throughput-Optimized)
- Benchmark Command:
python3 -m sglang.bench_serving \
--backend sglang \
--model meta-llama/Llama-4-Maverick-17B-128E-Instruct \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 500 \
--max-concurrency 100 \
--request-rate inf
- Test Results:
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 500
Benchmark duration (s): 90.95
Total input tokens: 249831
Total input text tokens: 249831
Total input vision tokens: 0
Total generated tokens: 252662
Total generated tokens (retokenized): 251625
Request throughput (req/s): 5.50
Input token throughput (tok/s): 2746.77
Output token throughput (tok/s): 2777.90
Peak output token throughput (tok/s): 3700.00
Peak concurrent requests: 109
Total token throughput (tok/s): 5524.67
Concurrency: 93.04
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 16924.17
Median E2E Latency (ms): 16294.85
---------------Time to First Token----------------
Mean TTFT (ms): 188.19
Median TTFT (ms): 128.96
P99 TTFT (ms): 534.81
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 33.63
Median TPOT (ms): 35.37
P99 TPOT (ms): 38.26
---------------Inter-Token Latency----------------
Mean ITL (ms): 33.19
Median ITL (ms): 27.66
P95 ITL (ms): 76.91
P99 ITL (ms): 78.82
Max ITL (ms): 268.17
==================================================
5.3 Accuracy Benchmark
5.3.1 GSM8K Benchmark
- Benchmark Command:
python3 -m sglang.test.few_shot_gsm8k --num-questions 200
- Llama-4-Scout-17B-16E-Instruct
Accuracy: 0.945
Invalid: 0.000
Latency: 12.731 s
Output throughput: 1595.418 token/s
- Llama-4-Maverick-17B-128E-Instruct
Accuracy: 0.895
Invalid: 0.000
Latency: 9.739 s
Output throughput: 2405.505 token/s