DeepSeekv4 Pro 0813 1.6T — GB300 NVL72 vs MI355X
Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) on DeepSeekv4 Pro 0813 1.6T. 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 →
GB300 NVL72 / MI355X on DeepSeekv4 Pro 0813 1.6T at 53 tok/s/user: 108101 / 9835 tok/s/chip, $0.01 / $0.04 per million tokens. GB300 NVL72 is 614% cheaper per token; GB300 NVL72 delivers 999% more tok/s/chip.
Around the middle of the 30–121 tok/s/user interactivity band, at 76 tok/s/user on DeepSeekv4 Pro 0813 1.6T: GB300 NVL72 runs 59078 tok/s/chip at $0.01/M tokens, MI355X runs 7848 at $0.05/M. GB300 NVL72 is 389% cheaper per token; GB300 NVL72 delivers 653% more tok/s/chip.
Setting 99 tok/s/user as the target on DeepSeekv4 Pro 0813 1.6T, GB300 NVL72 produces 41215 tok/s/chip ($0.02 per million tokens) and MI355X produces 3079 ($0.14). GB300 NVL72 is 769% cheaper per token; GB300 NVL72 delivers 1238% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:108100.6MI355X:9835.0 | GB300 NVL72:59077.8MI355X:7847.9 | GB300 NVL72:41215.0MI355X:3079.3 |
| Cost ($/M tok) | GB300 NVL72:$0.006MI355X:$0.042 | GB300 NVL72:$0.011MI355X:$0.053 | GB300 NVL72:$0.016MI355X:$0.135 |
| tok/s/MW | GB300 NVL72:50990856MI355X:4705719 | GB300 NVL72:27866889MI355X:3754979 | GB300 NVL72:19441015MI355X:1473356 |
| Concurrency | GB300 NVL72:~1080MI355X:~13 | GB300 NVL72:~709MI355X:~6 | GB300 NVL72:~414MI355X:~2 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
DeepSeek V4 Pro 0813 1.6T • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
DeepSeek V4 Pro 0813 1.6T • 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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)
1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.