Inco AI launches Day-0 support for GLM 5.3

Inco AI launched day-0 support for Z.ai's GLM 5.3 model, releasing DFlash 2 and NVFP4 checkpoints alongside an Inco Engine endpoint that delivers up to 4.4× throughput versus the native FP8 checkpoint with autoregressive decoding at concurrency 1. The NVFP4 checkpoint matches FP8 accuracy across evaluations, and the endpoint is priced at $1.40 per 1M input tokens, $0.26 per 1M cached input tokens, and $4.40 per 1M output tokens.

Inco AI launches Day-0 support for GLM 5.3 Overview overview GLM 5.3 https://z.ai/blog/glm-5.3 arrives today with day-0 serving support from Inco AI on TokenRouter https://www.tokenrouter.com/ compute. The release pairs Z.ai's https://z.ai/ new model with our core inference optimization technology: - A DFlash 2 checkpoint https://huggingface.co/incoai/GLM-5.3-DFlash2 for speculative decoding. - An NVFP4 checkpoint https://huggingface.co/incoai/GLM-5.3-NVFP4 for efficient Blackwell inference with native accuracy. Inco Engine https://platform.inco.ai/playground?model=glm-5.3 for end-to-end inference performance. As a day-0 launch partner, we are releasing both checkpoints alongside the model endpoint. Together, the checkpoints and Inco Engine deliver up to 4.4× throughput versus the native FP8 checkpoint https://huggingface.co/zai-org/GLM-5.3 with autoregressive decoding at concurrency 1. DFlash 2 for GLM 5.3 dflash-2-for-glm-53 DFlash 2 /blog/dflash2/ is a parallel drafter for speculative decoding: it predicts candidate tokens in one pass and lets the target model verify them as a block. Acceptance length AL measures how many tokens each draft–verify cycle yields, including the verifier's next token. A longer accepted block means fewer full target-model passes for the same output. DFlash 2 improves AL by keeping the parallel draft design while selecting a more coherent path through each position's candidates. Acceptance length is only half of the serving result: a drafter also adds work to each cycle. For GLM 5.3, we therefore report end-to-end decode throughput separately, comparing the model's native MTP path and DFlash 2 against autoregressive decoding. At concurrency 1, DFlash 2 reaches 383.3 output tok/s on MATH-500, 366.6 on GSM8K, and 363.7 on HumanEval. NVFP4 for Blackwell nvfp4-for-blackwell We are also releasing an NVFP4 checkpoint https://huggingface.co/incoai/GLM-5.3-NVFP4 for efficient Blackwell deployment, with accuracy matching the native FP8 checkpoint https://huggingface.co/zai-org/GLM-5.3 across the evaluation suite. | Precision | GPQA Diamond | AIME 2025 | MATH-500 | HLE | AA-LCR | |---|---|---|---|---|---| | FP8 | 91.1 | 94.3 | 95.6 | 35.9 | 73.6 | | NVFP4 | 91.2 | 95.1 | 95.2 | 35.2 | 73.0 | Get access get-access Try GLM 5.3 in the browser or connect through the Inco API. Included in preview - Browser playground - OpenAI and Anthropic API dialects - Streaming responses - 1M-token context window List pricing per 1M tokens- Input - $1.40 - Cached input - $0.26 - Output - $4.40 Get checkpoints get-checkpoints Compute for GLM 5.3 compute-for-glm-53 Compute for this launch came from TokenRouter https://www.tokenrouter.com/ . We trained the DFlash 2 drafter and produced the NVFP4 checkpoint on its Blackwell clusters, and we serve the day-0 endpoint from the same pool. You can reach the endpoint with an existing TokenRouter key. The bottom line the-bottom-line Ten days ago, we called DFlash 2 /blog/dflash2/ the first piece of an end-to-end serving stack. Launches like this are what the rest of it is for. GLM 5.3 shipped today, and the stack shipped with it: a DFlash 2 drafter, an NVFP4 checkpoint, and a live endpoint on Inco Engine. Together they serve the model at up to 4.4× the throughput of the native FP8 checkpoint with autoregressive decoding, measured at concurrency 1. If you want your model served like this, fine-tunes included, write to us: contact@inco.ai mailto:contact@inco.ai . Get updates get-updates One email when we ship something new. We will never share your email address.