# DSpark

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vLLM Blog Nov 12, 2026
vLLM serves Thinking Machines' Inkling 2 Small on day one: 594B total parameters, 17B active, about 630 tok/s for a single user
Thinking Machines' Inkling 2 Small gained vLLM support on its release day. It is a text-and-image-in, text-out mixture-of-experts model with 594B total and 17B active parameters, up to 1M tokens of context, and a bundled DSpark speculative-decoding drafter. vLLM fits it with kernel fusion, programmatic dependent launch, sequence parallelism and a Gluon attention kernel rewritten for the 6:1 GQA ratio, reaching about 630 tok/s for a single user at TP4 on GB200 with acceptance length 3.5, and also supports LoRA and reinforcement learning via SkyRL.
Thinking Machines' Inkling 2 Small gained vLLM support on its release day. It is a text-and-image-in, text-out mixture-of-experts model with 594B total and 17B active parameters, up to 1M tokens of context, and a bundled DSpark speculative-decoding drafter. vLLM fits it with kernel fusion, programmatic dependent launch, sequence parallelism and a Gluon attention kernel rewritten for the 6:1 GQA ratio, reaching about 630 tok/s for a single user at TP4 on GB200 with acceptance length 3.5, and also supports LoRA and reinforcement learning via SkyRL.
vLLM
Inkling 2 Small Kernel Fusion DSpark NVFP4
vLLM Blog Sep 15, 2026
vLLM trains a DSpark drafter for Kimi K3: math-reasoning interactivity rises from about 110 to about 435 tok/s/user
The vLLM Project's Speculators training library now supports Kimi K3, a 2.8-trillion-parameter model, and produced a five-layer, five-billion-parameter DSpark draft model that combines parallel block drafting with sequential correction. On math-reasoning workloads, single-stream interactivity rises from about 110 to about 435 tok/s/user, output throughput reaches up to about 3.5x at matched interactivity under concurrent load, and the macro-average acceptance length across nine evaluation domains is 4.11 tokens per verification round.
The vLLM Project's Speculators training library now supports Kimi K3, a 2.8-trillion-parameter model, and produced a five-layer, five-billion-parameter DSpark draft model that combines parallel block drafting with sequential correction. On math-reasoning workloads, single-stream interactivity rises from about 110 to about 435 tok/s/user, output throughput reaches up to about 3.5x at matched interactivity under concurrent load, and the macro-average acceptance length across nine evaluation domains is 4.11 tokens per verification round.
vLLM
DSpark Kimi K3 Speculative Decoding Mooncake Store

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vLLM serves Thinking Machines' Inkling 2 Small on day one: 594B total parameters, 17B active, about 630 tok/s for a single user

vLLM trains a DSpark drafter for Kimi K3: math-reasoning interactivity rises from about 110 to about 435 tok/s/user