InferenceX Puts GB300 First for MiniMax M2.7, but Only on a Fixed Chat Test

MiniMax pitches M2.7 for complex agent harnesses and software-engineering work. InferenceX’s new ranking answers a more contained question: on a single-turn chat workload, GB300 NVL72 delivered the highest measured throughput at 13,707 tokens per second per GPU.

GB200 NVL72 came second at 12,515 tokens per second per GPU, a 10% gap. B300, B200 and MI355X followed at 12,253, 11,606 and 7,692 tokens per second per GPU, respectively.

A shared responsiveness constraint

Each platform was read at a matched target of 50 tokens per second per user. The test used one chat turn with 8,000 input tokens and 1,000 output tokens. InferenceX excludes hardware without a measurement at that operating point, rather than comparing a system at a different user-speed tradeoff.

The result includes the serving stack

This is not a hardware-only comparison. All listed measurements use FP4 precision, while GB300 and GB200 use Dynamo vLLM; B300, B200 and MI355X use vLLM. InferenceX says it measures real hardware, sweeps concurrency to trace each platform’s throughput-versus-interactivity frontier, and reruns tests as engines and configurations change.

A speed result, not an agent verdict

MiniMax announced M2.7 in March and describes it as supporting complex agent harnesses, agent teams, dynamic tool search and software-engineering tasks. Those are broader patterns than the ranking’s one prompt-and-response exchange. The benchmark therefore establishes a leader for its specified serving case, not across every workload MiniMax associates with M2.7.

Speed and listed token cost also point in different directions. InferenceX puts GB300 at $0.047 per million tokens, versus $0.041 for GB200, using measured throughput and GPU-hour rates from SemiAnalysis’s AI Cloud total-cost-of-ownership model. The page reruns continuously, but its newest input result was dated June 13, so later engine releases or configurations could change the order.