GLM 5.3 744B · Chip comparison

GLM 5.3 744B — MI325X vs MI355X

Head-to-head AI inference benchmark comparison of MI325X (AMD CDNA 3) and MI355X (AMD CDNA 4) on GLM 5.3 744B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.

AgentX replays real coding-agent sessions rather than fixed-length prompts, so context grows turn over turn and most of each request is served from cache instead of being recomputed. That turns the comparison into a systems question: KV transfer between nodes, prefix-aware routing, and cache capacity all move the curve alongside raw chip throughput. Fixed-sequence workloads stay the clean baseline for kernel and silicon performance, so the two scenarios answer different questions about the same hardware. Learn more about AgentX

MI355X hits 11956 tok/s/chip for $0.03 per million tokens at 49 tok/s/user on GLM 5.3 744B. No MI325X data at this operating point.

MI355X: 7496 tok/s/chip, $0.06 per million tokens at 85 tok/s/user on GLM 5.3 744B. MI325X is unmeasured here.

At 122 tok/s/user on GLM 5.3 744B, MI355X delivers 4528 tok/s/chip at $0.09 per million tokens; MI325X hasn't been benchmarked at this target. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view →

Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Throughput (tok/s/chip)
MI325X:MI355X:11956.5
MI325X:MI355X:7496.5
MI325X:MI355X:4527.6
Cost ($/M tok)
MI325X:MI355X:$0.035
MI325X:MI355X:$0.056
MI325X:MI355X:$0.092
tok/s/MW
MI325X:MI355X:5720792
MI325X:MI355X:3586841
MI325X:MI355X:2166304
Concurrency
MI325X:MI355X:~10
MI325X:MI355X:~6
MI325X:MI355X:~2

Inference Performance

Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.