Qwen 3.5 397B-A17B — B300 vs MI355X
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) on Qwen 3.5 397B-A17B. 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 →
Setting 36 tok/s/user as the target on Qwen 3.5 397B-A17B, B300 produces 97865 tok/s/chip ($0.01 per million tokens) and MI355X produces 20767 ($0.02). B300 is 213% cheaper per token; B300 delivers 371% more tok/s/chip.
At 51 tok/s/user interactivity on Qwen 3.5 397B-A17B, B300 delivers 97865 tok/s/chip at $0.01 per million tokens; MI355X delivers 11102 tok/s/chip at $0.04. B300 is 485% cheaper per token; B300 delivers 782% more tok/s/chip at this point.
B300 posts 86673 tok/s/chip for $0.01 per million tokens at 65 tok/s/user on Qwen 3.5 397B-A17B; MI355X posts 7587 tok/s/chip for $0.05. B300 is 658% cheaper per token; B300 delivers 1042% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:97865.3MI355X:20767.1 | B300:97865.3MI355X:11102.0 | B300:86672.9MI355X:7586.9 |
| Cost ($/M tok) | B300:$0.006MI355X:$0.020 | B300:$0.006MI355X:$0.038 | B300:$0.007MI355X:$0.055 |
| tok/s/MW | B300:51508033MI355X:9936423 | B300:51508033MI355X:5311964 | B300:45617299MI355X:3630100 |
| Concurrency | B300:~52MI355X:~14 | B300:~52MI355X:~12 | B300:~43MI355X:~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 - Hyperscaler) vs. Interactivity
Qwen3.5 397B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
TCO $/chip/hr: H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68
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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