---
title: "Qwen3.6-27B"
metatags:
  description: "Best Practice for Qwen3.6-27B on Ascend NPU"
---

<Note>
This page focuses on optimal configuration and benchmark results for Qwen3.6-27B on the Ascend NPU. For environment setup, model weight download, feature configuration, and deployment instructions, etc., see the [Qwen3.6-27B Model Tutorial](/docs/hardware-platforms/ascend-npus/model-tutorials/qwen3_6_27b).

On A3 each card has 2 dies, so `--tp-size` is twice the card count; see [Ascend NPU Reference](/docs/hardware-platforms/ascend-npus/ascend_npu_reference#hardware) for details.
</Note>

### High Throughput

| Model | Hardware | Cards | Deploy Mode | Dataset | TPOT | Quantization | Configuration |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Qwen3.6-27B | Atlas 800I A3 | 1 | PD Mixed | 1024x1024 (30)+1024 | 50ms | BF16 | [Optimal Configuration](#qwen3-6-27b-1p-in1024x1024-30-out1024-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 1 | PD Mixed | 1080p_30+256 | 50ms | BF16 | [Optimal Configuration](#qwen3-6-27b-1p-in1080p-30-out256-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 1 | PD Mixed | 64k+1k (90% prefix cache hit rate) | 50ms | BF16 | [Optimal Configuration](#qwen3-6-27b-1p-in64k-out1k-prefix90-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 1 | PD Mixed | 3.5k+1.5k | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-6-27b-w8a8-1p-in3k5-out1k5-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 1 | PD Mixed | 64k+1k | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-6-27b-w8a8-1p-in64k-out1k-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 2 | PD Mixed | 128k+1k | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-6-27b-w8a8-2p-in128k-out1k-50ms) |
| Qwen3.6-27B | Atlas 800I A3 | 2 | PD Mixed | 16k+1k | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-6-27b-w8a8-2p-in16k-out1k-50ms) |

## Optimal Configuration

<a id="single-node-pd-mixed" title="Referenced by external docs. Verify before removing."></a>

### Qwen3.6-27B 1P IN1024X1024 30 OUT1024 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 1

**Deploy Mode**: PD Mixed

**Quantization**: BF16

**Dataset**: 1024x1024 (30)+1024

*Format: resolution (input tokens) + output tokens*

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=150
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_VIT_ENABLE_CUDA_GRAPH=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 2 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 52000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 50 \
    --max-mamba-cache-size 60 \
    --mem-fraction-static 0.76 \
    --cuda-graph-bs 2 4 8 16 24 32 40 42 45 50 \
    --enable-multimodal \
    --mm-attention-backend ascend_attn \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --mm-enable-dp-encoder \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 48 \
    --random-input-len 30 \
    --random-output-len 1024 \
    --num-prompts 48 \
    --random-range-ratio 1
```

### Qwen3.6-27B 1P IN1080P 30 OUT256 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 1

**Deploy Mode**: PD Mixed

**Quantization**: BF16

**Dataset**: 1080p_30+256

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=150
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_VIT_ENABLE_CUDA_GRAPH=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 2 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 48000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 30 \
    --max-mamba-cache-size 40 \
    --mem-fraction-static 0.76 \
    --cuda-graph-bs 2 4 8 16 24 28 30 \
    --enable-multimodal \
    --mm-attention-backend ascend_attn \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --mm-enable-dp-encoder \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 30 \
    --random-input-len 30 \
    --random-output-len 256 \
    --num-prompts 120 \
    --random-range-ratio 1
```

### Qwen3.6-27B 1P IN64K OUT1K PREFIX90 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 1

**Deploy Mode**: PD Mixed

**Quantization**: BF16

**Dataset**: 64k+1k (90% prefix cache hit rate)

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GDN_ATTN_BACKEND_TRITON=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 2 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size 32768 \
    --max-prefill-tokens 32768 \
    --mamba-radix-cache-strategy extra_buffer \
    --trust-remote-code \
    --max-running-requests 20 \
    --max-mamba-cache-size 160 \
    --mem-fraction-static 0.82 \
    --cuda-graph-bs 1 2 5 10 15 17 19 20 \
    --enable-prefill-delayer \
    --prefill-delayer-queue-min-ratio 0.7 \
    --prefill-delayer-max-delay-ms 20000 \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `generated-shared-prefix` dataset with 90% cache hit (`repeat_rate = 0.9`):
`--gsp-system-prompt-len 57600` = `int(64000 * 0.9)` is the shared prefix portion.
`--gsp-question-len 6400` = `int(64000 * (1 - 0.9))` is the unique per-request suffix.
`--gsp-num-groups 1` keeps all requests in one prefix group for maximum cache reuse.

```bash Command
python -m sglang.bench_serving \
    --dataset-name generated-shared-prefix \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --gsp-num-groups 1 \
    --gsp-prompts-per-group 80 \
    --gsp-system-prompt-len 57600 \
    --gsp-question-len 6400 \
    --gsp-output-len 1000 \
    --max-concurrency 20 \
    --num-prompts 80 \
    --request-rate inf
```

### Qwen3.6-27B W8A8 1P IN3K5 OUT1K5 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 1

**Deploy Mode**: PD Mixed

**Quantization**: W8A8 INT8

**Dataset**: 3.5k+1.5k

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=0
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=130
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 2 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 60000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 64 \
    --max-mamba-cache-size 74 \
    --mem-fraction-static 0.7 \
    --cuda-graph-bs 2 8 16 32 40 45 50 54 \
    --enable-multimodal \
    --quantization modelslim \
    --mm-attention-backend ascend_attn \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 54 \
    --random-input-len 3500 \
    --random-output-len 1500 \
    --num-prompts 216 \
    --random-range-ratio 1
```

### Qwen3.6-27B W8A8 1P IN64K OUT1K 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 1

**Deploy Mode**: PD Mixed

**Quantization**: W8A8 INT8

**Dataset**: 64k+1k

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 2 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 48000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 6 \
    --max-mamba-cache-size 16 \
    --mem-fraction-static 0.6 \
    --cuda-graph-bs 1 2 4 5 6 \
    --quantization modelslim \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 6 \
    --random-input-len 64000 \
    --random-output-len 1000 \
    --num-prompts 12 \
    --random-range-ratio 1
```

### Qwen3.6-27B W8A8 2P IN128K OUT1K 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 2

**Deploy Mode**: PD Mixed

**Quantization**: W8A8 INT8

**Dataset**: 128k+1k

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=20
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 4 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 74000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 6 \
    --max-mamba-cache-size 7 \
    --mem-fraction-static 0.63 \
    --cuda-graph-bs 1 2 4 5 6 \
    --enable-multimodal \
    --quantization modelslim \
    --mm-attention-backend ascend_attn \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 4 \
    --random-input-len 128000 \
    --random-output-len 1000 \
    --num-prompts 16 \
    --random-range-ratio 1
```

### Qwen3.6-27B W8A8 2P IN16K OUT1K 50ms

**Model**: Qwen3.6-27B

**Hardware**: Atlas 800I A3

**Cards**: 2

**Deploy Mode**: PD Mixed

**Quantization**: W8A8 INT8

**Dataset**: 16k+1k

**TPOT**: 50ms

#### Model Deployment

```bash Command
# ============================================================
# Before running, update the following variables:
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

MODEL_PATH=/path/to/model-weights
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export ASCEND_USE_FIA=1
export GDN_ATTN_BACKEND_TRITON=1
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_OP_EXPANSION_MODE=AIV
export HCCL_SOCKET_IFNAME=<network-interface>
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=100
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --host 127.0.0.1 --port 6688 \
    --tp-size 4 \
    --nnodes 1 \
    --attention-backend ascend \
    --device npu \
    --chunked-prefill-size -1 \
    --max-prefill-tokens 58000 \
    --disable-radix-cache \
    --trust-remote-code \
    --max-running-requests 29 \
    --max-mamba-cache-size 58 \
    --mem-fraction-static 0.68 \
    --cuda-graph-bs 1 2 8 12 16 20 24 26 28 29 \
    --quantization modelslim \
    --dtype bfloat16 \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

#### Benchmark

We tested it based on the `RANDOM` dataset.

```bash Command
python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 29 \
    --random-input-len 16000 \
    --random-output-len 1000 \
    --num-prompts 116 \
    --random-range-ratio 1
```
