Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and MI300X (AMD CDNA 3) on DeepSeek R1. 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.
Near the low end of the 14–51 tok/s/user interactivity band, at 23 tok/s/user on DeepSeek R1: B300 runs 7185 tok/s/chip at $0.09/M tokens, MI300X runs 725 at $0.36/M. B300 is 316% cheaper per token; B300 delivers 891% more tok/s/chip.
Setting 32 tok/s/user as the target on DeepSeek R1, B300 produces 6746 tok/s/chip ($0.09 per million tokens) and MI300X produces 601 ($0.44). B300 is 372% cheaper per token; B300 delivers 1022% more tok/s/chip.
At 42 tok/s/user interactivity on DeepSeek R1, B300 delivers 6225 tok/s/chip at $0.10 per million tokens; MI300X delivers 406 tok/s/chip at $0.65. B300 is 544% cheaper per token; B300 delivers 1432% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:7185.3MI300X:725.2 | B300:6746.3MI300X:601.1 | B300:6225.3MI300X:406.3 |
| Cost ($/M tok) | B300:$0.087MI300X:$0.364 | B300:$0.093MI300X:$0.439 | B300:$0.101MI300X:$0.650 |
| tok/s/MW | B300:3781732MI300X:521723 | B300:3550706MI300X:432443 | B300:3276461MI300X:292288 |
| Concurrency | B300:~1574MI300X:~30 | B300:~765MI300X:~18 | B300:~411MI300X:~10 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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.
Matching measurements exist, but their chip series are hidden.
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DeepSeek R1 0528 671B 8K / 1K Total Tokens per $1 TCO vs. Interactivity