-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdocker-compose.bench-all6.yml
More file actions
186 lines (179 loc) · 5.35 KB
/
Copy pathdocker-compose.bench-all6.yml
File metadata and controls
186 lines (179 loc) · 5.35 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
# ============================================================================
# Full 6-model showdown — MoE x3 vs Dense x3
# GPU 0: RedHatAI Gemma4-26B MoE (official) port 8016
# GPU 1: Huihui Gemma4-26B MoE (abliterated) port 8017
# GPU 2: Jiunsong SuperGemma4-26B MoE (enhanced) port 8018
# GPU 3: Qwen3.5-27B Dense port 8019
# GPU 4: Qwopus3.5-27B Dense (Opus distilled) port 8020
# GPU 5: Gemma4-31B Dense port 8021
# All: FP8 KV, 128K context
# ============================================================================
services:
moe-redhat:
image: lna-lab/gemma4-inference:latest
container_name: moe-redhat
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=0
- MODEL_PATH=/models/current
- PORT=8016
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=16
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/RedHatAI/gemma-4-26B-A4B-it-NVFP4:/models/current:ro
ports:
- "8016:8016"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["0"]
capabilities: [gpu]
shm_size: '16gb'
moe-huihui:
image: lna-lab/gemma4-inference:latest
container_name: moe-huihui
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=1
- MODEL_PATH=/models/current
- PORT=8017
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=16
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated-NVFP4-llmc:/models/current:ro
ports:
- "8017:8017"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["1"]
capabilities: [gpu]
shm_size: '16gb'
moe-jiunsong:
image: lna-lab/gemma4-inference:latest
container_name: moe-jiunsong
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=2
- MODEL_PATH=/models/current
- PORT=8018
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=16
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/Jiunsong/supergemma4-26b-abliterated-multimodal-NVFP4:/models/current:ro
ports:
- "8018:8018"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["2"]
capabilities: [gpu]
shm_size: '16gb'
dense-qwen:
image: lna-lab/gemma4-inference:latest
container_name: dense-qwen
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=3
- MODEL_PATH=/models/current
- PORT=8019
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=8
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/huihui-ai/Huihui-Qwen3.5-27B-abliterated-NVFP4:/models/current:ro
ports:
- "8019:8019"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["3"]
capabilities: [gpu]
shm_size: '16gb'
dense-qwopus:
image: lna-lab/gemma4-inference:latest
container_name: dense-qwopus
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=4
- MODEL_PATH=/models/current
- PORT=8020
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=8
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/huihui-ai/Huihui-Qwopus3.5-27B-v3-abliterated-NVFP4:/models/current:ro
ports:
- "8020:8020"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["4"]
capabilities: [gpu]
shm_size: '16gb'
dense-gemma31:
image: lna-lab/gemma4-inference:latest
container_name: dense-gemma31
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=5
- MODEL_PATH=/models/current
- PORT=8021
- MAX_MODEL_LEN=131072
- MAX_NUM_SEQS=4
- GPU_MEMORY_UTILIZATION=0.95
- TENSOR_PARALLEL_SIZE=1
- QUANTIZATION=auto
- VLLM_CUDA_GRAPH_MODE=piecewise
- TRINITY_TURBO_ENABLED=0
volumes:
- /media/tonoken/Optane_DATA/Models/huihui-ai/Huihui-gemma-4-31B-it-abliterated-v2-NVFP4:/models/current:ro
ports:
- "8021:8021"
command: ["--kv-cache-dtype", "fp8"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["5"]
capabilities: [gpu]
shm_size: '16gb'