OCI GPU Quick Starts: AMD MI355X Instinct GPUs

This document provides hardware specifications, supported OS images, onboarding verification, sample benchmarks, and best-practices for OCI deployments using the AMD MI355X GPU bare-metal shape.

MI355X shapes represent a specialized class of heterogeneous accelerated compute in Oracle Cloud Infrastructure (OCI), built around the AMD Instinct MI355X GPU. These shapes provide 8 MI355X GPUs per node, each with 288 GiB of high-bandwidth HBM memory, targeting large-scale AI/ML workloads and HPC.

MI355X systems leverage AMD’s XGMI (Inter-GPU Express Global Memory Interconnect) for direct GPU-to-GPU connectivity within each node. On a typical MI355X system, all 8 GPUs are fully connected via XGMI, providing a single-hop, high-bandwidth, low-latency peer-to-peer path between any pair of GPUs. This topology is symmetrical, meaning all GPUs enjoy equal connectivity for workloads that depend on frequent collective or point-to-point communication.

At a Glance

Shape family: MI355X
GPU configuration: 8 x AMD Instinct MI355X
Recommended OS baseline: Oracle Linux 9+ or Ubuntu Linux 22.04+
Recommended software baseline: ROCm 7.1+, RCCL 2.26.6, OFED 25.07-0.9.7
Primary verification command: amd-smi
Operational profile: single-node and scale-out AI/ML and HPC with XGMI topology

When To Use This Shape

Use this shape when you need a current-generation AMD GPU platform with strong single-node XGMI performance and an increasingly mature scale-out software path.

It is best suited for teams validating ROCm-first workflows, large-memory AMD inference or training stacks, and environments that may need to compare standard MI355X against the Pollara variant.

First 15 Minutes

Launch the shape with one of the approved images from the Provided Images table.
Run amd-smi and confirm all eight MI355X GPUs are visible.
Start the ROCm container smoke test from Hello World Verification.
Run the first single-node RCCL check before moving on to model-serving or OKE-specific flows.

Hardware Specifications
When To Use This Shape
First 15 Minutes
Recommended Operating Systems
Hello World Verification
Performance Benchmarks
- RCCL & Model Inference Performance
OKE GPU Getting Started
Troubleshooting
Further Reading & Support

Hardware Specifications

Shape Name	GPU Model	GPUs/Node	GPU Memory (GB/GPU)	GPU Memory Total	CPU	# of CPUs	System Memory	Local Storage	Host NIC	RDMA (ROCe) NICs
BM.GPU.MI355x.v0.8	AMD MI355X	8	288	8x288GB = 2.3 TB	2 x AMD TURIN 9575F 3.3Ghz	128 Cores	3TB DDR	8xNVMe 7.68TB/disk = 61.44TB	NVIDIA CX-7 2x200GBps = 400GBps	AMD Pensando™ AI NIC, 8 1x400Gb/s Ethernet=3.2 Tb/s
BM.GPU.MI355x.v1.8	AMD MI355X	8	288	8x288GB = 2.3 TB	2 x AMD TURIN 9575F 3.3Ghz	128 Cores	3TB DDR	8xNVMe 7.68TB/disk = 61.44TB	NVIDIA CX-7 2x200GBps = 400GBps	Mellanox ConnectX-7, 8 1x400Gb/s=3.2 Tb/s

See the OCI Compute Shapes Docs for up-to-date details.

Recommended Operating Systems

• Oracle Linux 9+
• Ubuntu Linux 24.04+

Recommended Software Version

• ROCm Version: 7.1+
• RCCL: 2.26.6
• OFED: 25.07-0.9.7
• Oracle Cloud Agent 1.54.0+
• HPCX 2.22+

Custom OS image Creation with Packer

To build your images using packer clone the OCI HPC Images repo and run the commands found there OCI HPC Images GitHub Repo.

Provided Images

It is recommended to use Oracle-provided or organizationally-approved images for security and supportability. See OS Images Table for region-specific OCID's and version info.

OS Version	Image Packer Build Details	OCI Platform Image Link	Driver Versions
OCI GPU AI Image with Oracle Linux 9	`Oracle-Linux-9.6-RHCK-DOCA-OFED-3.2.1-AMD-ROCM-643`	PAR Link	ROCm 6.4.3, DOCA OFED 3.2.1, OCA 1.57.0
OCI GPU AI Image with Oracle Linux 9	`Oracle-Linux-9.7-RHCK-DOCA-OFED-3.2.1-AMD-ROCM-72`	PAR Link	ROCm 7.2, DOCA OFED 3.2.1, OCA 1.57.0
OCI GPU AI Image with Ubuntu Linux 22.04	`Canonical-Ubuntu-22.04-DOCA-OFED-3.2.1-AMD-ROCM-643`	PAR Link	ROCm 6.4.3, DOCA OFED 3.2.1, OCA 1.57.0
OCI GPU AI Image with Ubuntu Linux 22.04	`Canonical-Ubuntu-22.04-DOCA-OFED-3.2.1-AMD-ROCM-72`	PAR Link	ROCm 7.2, DOCA OFED 3.2.1, OCA 1.57.0
OCI GPU AI Image with Ubuntu Linux 24.04	`Canonical-Ubuntu-24.04-6.8-DOCA-OFED-3.2.1-AMD-ROCM-643`	PAR Link	ROCm 6.4.3, DOCA OFED 3.2.1, Kernel 6.8, OCA 1.57.0
OCI GPU AI Image with Ubuntu Linux 24.04	`Canonical-Ubuntu-24.04-6.8-DOCA-OFED-3.2.1-AMD-ROCM-72`	PAR Link	ROCm 7.2, DOCA OFED 3.2.1, Kernel 6.8, OCA 1.57.0
OCI GPU AI Image with Ubuntu Linux 24.04	`Canonical-Ubuntu-24.04-6.14-DOCA-OFED-3.2.1-AMD-ROCM-72`	PAR Link	ROCm 7.2, DOCA OFED 3.2.1, Kernel 6.14, OCA 1.57.0

Hello World Verification

You will need docker installed on the OS image before you can run this command. More information on different functions that can be run is found here

docker pull rocm/pytorch:latest
docker run -it \
    --cap-add=SYS_PTRACE \
    --security-opt seccomp=unconfined \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --ipc=host \
    --shm-size 8G \
    rocm/pytorch:latest

Performance Benchmarks

The section below has the base line numbers achieved and how to reproduce this with MI355X. When you run the same there is typically a variance of 5%-8% which is acceptable.

RCCL & Model Inference Performance

Collective communications (RCCL) results are for a single node.

All Reduce

/opt/rccl-tests/build/all_reduce_perf -b 512k -e 16G -f 2 -g 8

Results

# Collective test starting: all_reduce_perf
# nThread 1 nGpus 8 minBytes 524288 maxBytes 17179869184 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
#
rccl-tests: Version HEAD:e1b8a3a
# Using devices
#  Rank  0 Group  0 Pid   9632 on   GPU-6403 device  0 [0000:72:00] AMD Instinct MI355X
#  Rank  1 Group  0 Pid   9632 on   GPU-6403 device  1 [0000:0a:00] AMD Instinct MI355X
#  Rank  2 Group  0 Pid   9632 on   GPU-6403 device  2 [0000:5a:00] AMD Instinct MI355X
#  Rank  3 Group  0 Pid   9632 on   GPU-6403 device  3 [0000:23:00] AMD Instinct MI355X
#  Rank  4 Group  0 Pid   9632 on   GPU-6403 device  4 [0000:f1:00] AMD Instinct MI355X
#  Rank  5 Group  0 Pid   9632 on   GPU-6403 device  5 [0000:8b:00] AMD Instinct MI355X
#  Rank  6 Group  0 Pid   9632 on   GPU-6403 device  6 [0000:d9:00] AMD Instinct MI355X
#  Rank  7 Group  0 Pid   9632 on   GPU-6403 device  7 [0000:a4:00] AMD Instinct MI355X
#                                                              out-of-place                       in-place          
#       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
#        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)       
  8589934592    2147483648     float     sum      -1    36956  232.43  406.76      0    36911  232.72  407.26      0
 17179869184    4294967296     float     sum      -1    73913  232.43  406.76      0    73917  232.42  406.73      0
# Errors with asterisks indicate errors that have exceeded the maximum threshold.
# Out of bounds values : 0 OK
# Avg bus bandwidth    : 273.886 
#
# Collective test concluded: all_reduce_perf

All 2 All

/opt/rccl-tests/build/alltoall_perf -b 512k -e 16G -f 2 -g 8

Results

# Collective test starting: alltoall_perf
# nThread 1 nGpus 8 minBytes 524288 maxBytes 17179869184 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
#
rccl-tests: Version HEAD:e1b8a3a
# Using devices
#  Rank  0 Group  0 Pid   9878 on   GPU-6403 device  0 [0000:72:00] AMD Instinct MI355X
#  Rank  1 Group  0 Pid   9878 on   GPU-6403 device  1 [0000:0a:00] AMD Instinct MI355X
#  Rank  2 Group  0 Pid   9878 on   GPU-6403 device  2 [0000:5a:00] AMD Instinct MI355X
#  Rank  3 Group  0 Pid   9878 on   GPU-6403 device  3 [0000:23:00] AMD Instinct MI355X
#  Rank  4 Group  0 Pid   9878 on   GPU-6403 device  4 [0000:f1:00] AMD Instinct MI355X
#  Rank  5 Group  0 Pid   9878 on   GPU-6403 device  5 [0000:8b:00] AMD Instinct MI355X
#  Rank  6 Group  0 Pid   9878 on   GPU-6403 device  6 [0000:d9:00] AMD Instinct MI355X
#  Rank  7 Group  0 Pid   9878 on   GPU-6403 device  7 [0000:a4:00] AMD Instinct MI355X
#
#                                                              out-of-place                       in-place          
#       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
#        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)       
 8589934592     268435456     float    none      -1    20837  412.23  360.71      0    20817  412.64  361.06    N/A
 17179869184     536870912     float    none      -1    41593  413.05  361.41      0    41618  412.80  361.20    N/A

 # Errors with asterisks indicate errors that have exceeded the maximum threshold.
# Out of bounds values : 0 OK
# Avg bus bandwidth    : 236.187 
#
# Collective test concluded: alltoall_perf

LLM Inference Performance Numbers

The below output is for vLLM Throughput running the below command and the container release. For latest container version refer here.

Example output:

docker pull rocm/7.x-preview:rocm7.2_preview_ubuntu_22.04_vlm_0.10.1_instinct_20251029

While vLLM can download model weights at runtime, it’s recommended to download ahead of time. You will need:

A valid Hugging Face access token. Remember to set HF_TOKEN to your access token.

Access granted to the specific model from your Hugging Face account

model=openai/gpt-oss-120b

pip install huggingface_hub[cli] hf_transfer hf_xet
HF_HUB_ENABLE_HF_TRANSFER=1 \
HF_HOME=/data/huggingface-cache \
HF_TOKEN="<HF_TOKEN>" \ # Replace with your HF_TOKEN Hugging Face access token.
huggingface-cli download ${model} --exclude "original/*"

Start the container on MI355X. You will be inside the container once this command is successful.

docker run -it \
  --ipc=host \
  --network=host \
  --privileged \
  --cap-add=CAP_SYS_ADMIN \
  --device=/dev/kfd \
  --device=/dev/dri \
  --cap-add=SYS_PTRACE \
  --security-opt seccomp=unconfined \
  -v /data:/data \
  -e HF_HOME=/data/huggingface-cache \
  -e HF_HUB_OFFLINE=1 \
  --name vllm-server \
  rocm/7.x-preview:rocm7.2_preview_ubuntu_22.04_vlm_0.10.1_instinct_20251029

Start the vLLM server

model=openai/gpt-oss-120b
max_model_len=10368           # 1.125 x (input sequence length + output sequence length); e.g. 1.125 x (8192 + 1024) = 10368.
max_seq_len_to_capture=10368  # Beneficial to set this to max_model_len.
max_num_seqs=1024             # Set to max_concurrency of the client to get better throughput.
tensor_parallel_size=8

export VLLM_USE_AITER_UNIFIED_ATTENTION=1
export VLLM_ROCM_USE_AITER_MHA=0
export VLLM_ROCM_USE_AITER_FUSED_MOE_A16W4=1

vllm serve ${model} \
    --port 8000 \
    --swap-space 64 \
    --max-model-len ${max_model_len} \
    --tensor-parallel-size ${tensor_parallel_size} \
    --max-num-seqs ${max_num_seqs} \
    --gpu-memory-utilization 0.95 \
    --max-seq-len-to-capture ${max_seq_len_to_capture} \
    --compilation-config '{"compile_sizes":[1,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,256,512,1024,2048,8192] , "cudagraph_capture_sizes":[1,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,136,144,152,160,168,176,184,192,200,208,216,224,232,240,248,256,264,272,280,288,296,304,312,320,328,336,344,352,360,368,376,384,392,400,408,416,424,432,440,448,456,464,472,480,488,496,504,512,520,528,536,544,552,560,568,576,584,592,600,608,616,624,632,640,648,656,664,672,680,688,696,704,712,720,728,736,744,752,760,768,776,784,792,800,808,816,824,832,840,848,856,864,872,880,888,896,904,912,920,928,936,944,952,960,968,976,984,992,1000,1008,1016,1024,2048,4096,8192] , "cudagraph_mode": "FULL_AND_PIECEWISE"}' \
    --block-size=64 \
    --no-enable-prefix-caching \
    --async-scheduling

 # Wait for model to load and server is ready to accept requests.

Open another terminal on the same machine, connect to your running vllm-server container, and run the benchmark with the appropriate options. For example:

# Connect to server
docker exec -it vllm-server bash

Inside the container

# Run the client benchmark
model=openai/gpt-oss-120b
input_tokens=1024 # you can toggle the sizes here
output_tokens=1024 #you can toggle the sizes here
max_concurrency=4
num_prompts=32

python3 /app/vllm/benchmarks/benchmark_serving.py --host localhost --port 8000 \
    --model ${model} \
    --dataset-name random \
    --random-input-len ${input_tokens} \
    --random-output-len ${output_tokens} \
    --max-concurrency ${max_concurrency} \
    --num-prompts ${num_prompts} \
    --percentile-metrics ttft,tpot,itl,e2el \
    --ignore-eos

OKE GPU Getting Started

Create OKE Cluster via OCI Console or CLI.
Add GPU Node Pool as self managed node: More info here. You can use the same OS Image listed above and bootstrap the kubernetes components and registration to Kubernetes control plane. More abut adding it as cloud init script here
Install AMD Device Plugin on OKE:

kubectl apply -f https://github.com/AMD/k8s-device-plugin/raw/main/AMD-device-plugin.yml

Run GPU-powered Kubernetes workloads: Example pod resource spec:

AMD RVS Test Yaml

Verify with Hello World ROCm container pod is deployable and you can see the AMD GPUs within it

kubectl create -f ./amd/MI355X/k8s/hello-world-rocm-oke.yaml

Output

kubectl logs amd-smi

============================================= ROCm System Management Interface =============================================						
======================================================= Concise Info =======================================================						
Device  Node  IDs              Temp        Power     Partitions          SCLK     MCLK     Fan  Perf  PwrCap   VRAM%  GPU%						
              (DID,     GUID)  (Junction)  (Socket)  (Mem, Compute, ID)                                                     						
============================================================================================================================						
0       3     0x75a3,   21010  54.0°C      1224.0W   NPS1, SPX, 0        2358Mhz  2000Mhz  0%   auto  1400.0W  98%    93% 						
1       5     0x75a3,   56525  54.0°C      1231.0W   NPS1, SPX, 0        2366Mhz  2000Mhz  0%   auto  1400.0W  98%    95% 						
2       4     0x75a3,   28206  55.0°C      1219.0W   NPS1, SPX, 0        2361Mhz  2000Mhz  0%   auto  1400.0W  98%    94% 						
3       2     0x75a3,   28720  56.0°C      1249.0W   NPS1, SPX, 0        2340Mhz  2000Mhz  0%   auto  1400.0W  98%    96% 						
4       7     0x75a3,   25266  52.0°C      1275.0W   NPS1, SPX, 0        2374Mhz  2000Mhz  0%   auto  1400.0W  98%    100%						
5       9     0x75a3,   20206  56.0°C      1267.0W   NPS1, SPX, 0        2377Mhz  2000Mhz  0%   auto  1400.0W  98%    100%						
6       8     0x75a3,   16143  54.0°C      1273.0W   NPS1, SPX, 0        2382Mhz  2000Mhz  0%   auto  1400.0W  98%    100%						
7       6     0x75a3,   8465   54.0°C      1317.0W   NPS1, SPX, 0        2374Mhz  2000Mhz  0%   auto  1400.0W  98%    100%						
============================================================================================================================						
=================================================== End of ROCm SMI Log ====================================================	

AMD SMI Tool: 24.6.2+2b02a07 | AMD SMI Library version: 24.6.2.0 | ROCm version: 7.1.2

Troubleshooting

Here you can find suggested troubleshooting methods.

Confirm Hardware & Drivers
Health Checks
ROCM AGFHC
OCI GPU Scanner

Confirm Hardware & Drivers

amd-smi

============================================= ROCm System Management Interface =============================================
======================================================= Concise Info =======================================================
Device  Node  IDs              Temp        Power     Partitions          SCLK     MCLK     Fan  Perf  PwrCap   VRAM%  GPU%  
              (DID,     GUID)  (Junction)  (Socket)  (Mem, Compute, ID)                                                     
============================================================================================================================
0       3     0x75a3,   21010  44.0°C      257.0W    NPS1, SPX, 0        797Mhz   2000Mhz  0%   auto  1400.0W  17%    1%    
1       5     0x75a3,   56525  44.0°C      272.0W    NPS1, SPX, 0        1027Mhz  2000Mhz  0%   auto  1400.0W  17%    2%    
2       4     0x75a3,   28206  45.0°C      260.0W    NPS1, SPX, 0        771Mhz   2000Mhz  0%   auto  1400.0W  17%    1%    
3       2     0x75a3,   28720  43.0°C      256.0W    NPS1, SPX, 0        496Mhz   2000Mhz  0%   auto  1400.0W  17%    1%    
4       7     0x75a3,   25266  41.0°C      259.0W    NPS1, SPX, 0        712Mhz   2000Mhz  0%   auto  1400.0W  17%    1%    
5       9     0x75a3,   20206  45.0°C      288.0W    NPS1, SPX, 0        2386Mhz  2000Mhz  0%   auto  1400.0W  17%    1%    
6       8     0x75a3,   16143  43.0°C      285.0W    NPS1, SPX, 0        2404Mhz  2000Mhz  0%   auto  1400.0W  17%    1%    
7       6     0x75a3,   8465   44.0°C      260.0W    NPS1, SPX, 0        722Mhz   2000Mhz  0%   auto  1400.0W  17%    1%    
============================================================================================================================
=================================================== End of ROCm SMI Log ====================================================

numactl --hardware

numactl --hardware
available: 2 nodes (0-1)
node 0 cpus: 0 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 63 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
node 0 size: 1547872 MB
node 0 free: 1352749 MB
node 1 cpus: 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255
node 1 size: 1548129 MB
node 1 free: 1456225 MB
node distances:
node   0   1 
  0:  10  32 
  1:  32  10

Health Checks

The AMDs ROCm Validation Suite (RVS) is a system validation and diagnostics tool for monitoring, stress testing, detecting, and troubleshooting issues that affect the functionality and performance of AMD GPUs operating in a high-performance/AI/ML computing environment. RVS is enabled using the ROCm software stack on a compatible software and hardware platform and is part of the OS images from OCI.

cd /opt/rocm/bin
./rvs

Result

+=====================================================================+
|                 ROCm Validation Suite (RVS) Summary                 |
+=====================================================================+
|                           System Overview                           |
+---------------------------------------------------------------------+
| Operating System                 | Ubuntu 24.04.3 LTS               |
| RVS version                      | 1.3.0                            |
| ROCm version                     | 7.0.2-56                         |
| amdgpu version                   | 6.14.14                          |
| GPUs                             | 8                                |
+---------------------------------------------------------------------+
| GPU Name - GPU ID                |                                  |
| ID - Node ID - BDF               |                                  |
+---------------------------------------------------------------------+
| AMD Instinct MI355X - 21010      | AMD Instinct MI355X - 56525      |
| 0 - 3 - 0000:0a:00.0             | 1 - 5 - 0000:23:00.0             |
+---------------------------------------------------------------------+
| AMD Instinct MI355X - 28206      | AMD Instinct MI355X - 28720      |
| 2 - 4 - 0000:5a:00.0             | 3 - 2 - 0000:72:00.0             |
+---------------------------------------------------------------------+
| AMD Instinct MI355X - 25266      | AMD Instinct MI355X - 20206      |
| 4 - 7 - 0000:8b:00.0             | 5 - 9 - 0000:a4:00.0             |
+---------------------------------------------------------------------+
| AMD Instinct MI355X - 16143      | AMD Instinct MI355X - 8465       |
| 6 - 8 - 0000:d9:00.0             | 7 - 6 - 0000:f1:00.0             |
+=====================================================================+
| Action Name                      | Module         | Result          |
+=====================================================================+
| action_1                         | GPUP           | PASS            |
| action_2                         | PEQT           | PASS            |
| action_3                         | PEBB           | PASS            |
| action_4                         | PBQT           | PASS            |
| action_5                         | IET            | PASS            |
| action_6                         | GST            | PASS            |
| action_7                         | BABEL          | PASS            |
| action_8                         | MEM            | PASS            |
| action_9                         | PESM           | PASS            |
+---------------------------------------------------------------------+
[RESULT] [ 82145.821495] [module_terminate] PCIe monitoring ended after wait duration.

ROCM AGFHC

The recommended tool for identifying additional issues is ROCM AGFHC (AMD GPU Field Health Check). This tool is only made available to NDA OCI customers. Reach out to us to us to get access to this tool in container format. More about AGHFC..

OCI GPU Scanner

We recommend you use OCI GPU Scanner to manage the OKE cluster health and GPU health. GPU scanner also provies continu=ious health check and auto-remidiation. OCI GPU Scanner - Cluster Health Management Solution

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OCI GPU Quick Starts: AMD MI355X Instinct GPUs

At a Glance

When To Use This Shape

First 15 Minutes

Table of Contents

Hardware Specifications

Recommended Operating Systems

Recommended Software Version

Custom OS image Creation with Packer

Provided Images

Hello World Verification

Performance Benchmarks

RCCL & Model Inference Performance

All Reduce

All 2 All

LLM Inference Performance Numbers

OKE GPU Getting Started

Troubleshooting

Confirm Hardware & Drivers

Health Checks

ROCM AGFHC

OCI GPU Scanner

Further Reading & Support

AMD Technical Documents

OCI AMD 355X Blogs

OCI AMD 355X Technical Docs & Supported Solutions

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README-MI355X.md

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README-MI355X.md

File metadata and controls

OCI GPU Quick Starts: AMD MI355X Instinct GPUs

At a Glance

When To Use This Shape

First 15 Minutes

Table of Contents

Hardware Specifications

Recommended Operating Systems

Recommended Software Version

Custom OS image Creation with Packer

Provided Images

Hello World Verification

Performance Benchmarks

RCCL & Model Inference Performance

All Reduce

All 2 All

LLM Inference Performance Numbers

OKE GPU Getting Started

Troubleshooting

Confirm Hardware & Drivers

Health Checks

ROCM AGFHC

OCI GPU Scanner

Further Reading & Support

AMD Technical Documents

OCI AMD 355X Blogs

OCI AMD 355X Technical Docs & Supported Solutions