Qwen 3.5 397B-A17B · Speculative Decoding

TPU7x FP8: MTP vs Off Speculative Decoding

Speculative decoding comparison of MTP versus Off on TPU7x FP8 (Google Ironwood) running Qwen 3.5 397B-A17B. Throughput, cost, and interactivity differences across LLM workloads. Use the chart controls below to switch sequences and metrics — same interactions as the main inference chart.

MTP acceptance-rate comparability

MTP acceptance-rate implementations differ across inference engines. Points from different engines are not directly comparable on the same curve — throughput and cost at matched interactivity may reflect engine-level differences rather than pure speculative decoding gains. Interpret cross-engine comparisons with caution.

Off: 5664 tok/s/chip, $0.05 per million tokens at 59 tok/s/user on Qwen 3.5 397B-A17B (TPU7x FP8). MTP is unmeasured here.

At 111 tok/s/user on Qwen 3.5 397B-A17B (TPU7x FP8), Off delivers 3559 tok/s/chip at $0.08 per million tokens; MTP hasn't been benchmarked at this target.

Off hits 2468 tok/s/chip for $0.12 per million tokens at 163 tok/s/user on Qwen 3.5 397B-A17B (TPU7x FP8). No MTP data at this operating point. (Numbers reflect this URL's pinned 8k/1k · fp8 workload — changing sequence or model updates both the table and chart; the table stays pinned to this page's precision, so precision toggles in the controls affect the chart only.)

Qwen 3.5 397B-A17B: TPU7x FP8 MTP versus Off speculative decoding comparison at matched interactivity levels
TPU7x FP8 MTP versus Off speculative decoding comparison for this page's canonical default workload.
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)
MTP:—Off:5663.6
MTP:—Off:3559.1
MTP:—Off:2467.8
Cost ($/M tok)
MTP:—Off:$0.051
MTP:—Off:$0.080
MTP:—Off:$0.116
tok/s/MW
MTP:—Off:4692296
MTP:—Off:2948752
MTP:—Off:2044583
Concurrency
MTP:—Off:~45
MTP:—Off:~15
MTP:—Off:~7

Inference Performance

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

Benchmark Config