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Llama 3.3 70B · Chip comparison

Llama 3.3 70B — MI300X vs MI355X

Head-to-head AI inference benchmark comparison of MI300X (AMD CDNA 3) and MI355X (AMD CDNA 4) on Llama 3.3 70B. 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.

MI300X / MI355X on Llama 3.3 70B at 36 tok/s/user: 1823 / 4443 tok/s/chip, $0.14 / $0.09 per million tokens. MI355X is 54% cheaper per token; MI355X delivers 144% more tok/s/chip.

Around the middle of the 14–101 tok/s/user interactivity band, at 58 tok/s/user on Llama 3.3 70B: MI300X runs 1144 tok/s/chip at $0.23/M tokens, MI355X runs 2735 at $0.15/M. MI355X is 51% cheaper per token; MI355X delivers 139% more tok/s/chip.

Setting 80 tok/s/user as the target on Llama 3.3 70B, MI300X produces 679 tok/s/chip ($0.39 per million tokens) and MI355X produces 1225 ($0.34). MI355X is 14% cheaper per token; MI355X delivers 80% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 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)
MI300X:1822.9MI355X:4443.3
MI300X:1144.4MI355X:2735.2
MI300X:679.1MI355X:1225.3
Cost ($/M tok)
MI300X:$0.145MI355X:$0.094
MI300X:$0.231MI355X:$0.152
MI300X:$0.389MI355X:$0.340
tok/s/MW
MI300X:1311463MI355X:2125962
MI300X:823315MI355X:1308691
MI300X:488526MI355X:586278
Concurrency
MI300X:~27MI355X:~17
MI300X:~16MI355X:~25
MI300X:~8MI355X:~7

Inference Performance

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

Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity

Llama 3.3 70B Instruct • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™

TCO $/chip/hr: VR200: 3.61H100: 1.55H200: 1.59B200: 2.07B300: 2.52GB200: 2.26GB300: 2.79MI300X: 1.16MI325X: 1.32MI355X: 2.09RTX6000PRO: 0.75Jalapeño (Teacup): 1.56

Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model

Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate cost per million tokens per decode chip or per prefill chip, rather than per total chip count. This makes direct token cost comparison with aggregated configs not an apples-to-apples comparison.

Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate input throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct input throughput comparison with aggregated configs not an apples-to-apples comparison.

Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate output throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct output throughput comparison with aggregated configs not an apples-to-apples comparison.

Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate power per decode chip or per prefill chip, rather than per total chip count. This makes direct power comparison with aggregated configs not an apples-to-apples comparison.

Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate Joules per decode chip or per prefill chip, rather than per total chip count. This makes direct Joules per token comparison with aggregated configs not an apples-to-apples comparison.

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