MiniMax responded to SGLang including MiniMax H3 in the TPU support list of SGLang-TorchTPU, expressing thanks and saying it looks forward to more open-source work around efficient video inference. SGLang said that SGLang-TorchTPU was jointly built by SGLang, the Google TPU team, and the Meta PyTorch team, has been used for Meta's production inference, and supports models including Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, and MiniMax H3. There are currently no facts about subsequent developments.
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 400 ms first-token latency, and a generation speed of 230 tokens/s. The report also noted that components such as KV cache management and load balancing have not yet been fully open-sourced. The next day, DeepSeek published the DeepInfer technical report on Zhihu, disclosing internal details including the hardware selection for Prefill and Decode and the KVCache targets, and providing data showing a single cluster serving 155 trillion Tokens/day at $38 per card per hour at off-peak prices. The framework is currently open-sourced, but the KV cache system, Prefill/Decode disaggregation, and load balancing required for a complete service are not fully included in this open-source project.
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 4
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 400 ms first-token latency, and a generation speed of 230 tokens/s. The report also noted that components such as KV cache management and load balancing have not yet been fully open-sourced. The next day, DeepSeek published the DeepInfer technical report on Zhihu, disclosing internal details including the hardware selection for Prefill and Decode and the KVCache targets, and providing data showing a single cluster serving 155 trillion Tokens/day at $38 per card per hour at off-peak prices. The framework is currently open-sourced, but the KV cache system, Prefill/Decode disaggregation, and load balancing required for a complete service are not fully included in this open-source project.
On October 20, 2026, DeepSeek open-sourced its internal inference framework DeepInfer. According to Yuchen Jin, citing data disclosed by DeepSeek, a single cluster processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour (off-peak pricing); DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. A subsequent 36Kr report stated that its accompanying open-source deepseek-recipe supports NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. On October 21, swyx, citing Latent.Space data, said a single cluster processes 155T tokens per day with a 96% input cache hit rate, and argued that the lifetime value of each sold GPU has risen by about 30%. On October 24, Nathan Lambert said DeepSeek's single cluster processes 150T tokens per day, exceeding the combined open-source model inference volume of OpenRouter, Fireworks, Together, and BaseTen, and noted that the actual number of clusters is unknown; on the same day, LMSYS published a same-hardware benchmark comparison, stating that SGLang meets or exceeds DeepInfer on DeepInfer's own benchmark, with prefill throughput up to 1.04x and decode step length shortened by 1.1%–7.4% on 64x GB300.
On October 20, 2026, Zyphra released the second generation of inference optimizations, ZOE, adding it to the ZOE inference optimization series. According to information published by Zyphra Research, on AMD Instinct MI355X, GLM-5.3's time to first token (TTFT) at 512K context dropped from 178.8 seconds to 64.6 seconds, about 2.8x; at 90% prefix cache hit rate, it dropped from 42.3 seconds to 11.8 seconds, about 3.6x. Its CSA top-K selection HIP kernel raised DeepSeek-V4-Pro's single-user decoding throughput at 1M context from 43.5 to 61.1 tokens/s, about 1.40x. GLM-5.3's AMD MXFP4 quantization compressed the model from 756 GB to 410 GB, with benchmark scores such as AIME25 within 3 points of FP8. Currently this release is the only development with evidence, and there are no subsequent developments yet.
On October 20, 2026, Zyphra released the second generation of inference optimizations, ZOE, adding it to the ZOE inference optimization series. According to information published by Zyphra Research, on AMD Instinct MI355X, GLM-5.3's time to first token (TTFT) at 512K context dropped from 178.8 seconds to 64.6 seconds, about 2.8x; at 90% prefix cache hit rate, it dropped from 42.3 seconds to 11.8 seconds, about 3.6x. Its CSA top-K selection HIP kernel raised DeepSeek-V4-Pro's single-user decoding throughput at 1M context from 43.5 to 61.1 tokens/s, about 1.40x. GLM-5.3's AMD MXFP4 quantization compressed the model from 756 GB to 410 GB, with benchmark scores such as AIME25 within 3 points of FP8. Currently this release is the only development with evidence, and there are no subsequent developments yet.
On October 20, 2026, Zyphra released the second generation of inference optimizations, ZOE, adding it to the ZOE inference optimization series. According to information published by Zyphra Research, on AMD Instinct MI355X, GLM-5.3's time to first token (TTFT) at 512K context dropped from 178.8 seconds to 64.6 seconds, about 2.8x; at 90% prefix cache hit rate, it dropped from 42.3 seconds to 11.8 seconds, about 3.6x. Its CSA top-K selection HIP kernel raised DeepSeek-V4-Pro's single-user decoding
Ant inclusionAI has launched the sglang_ling_v3 repository on GitHub, whose contents are based on the open-source inference framework SGLang. The repository's README introduces that SGLang targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The README also shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes a built-in image and video generation engine, SGLang Diffusion. The repository has currently been released, with no further development activity yet.
Ant inclusionAI has launched the sglang ling v3 repository on GitHub, whose contents are based on the open-source inference framework SGLang. The repository's README introduces that SGLang targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The README also shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes a built-in image and video generation engine, SGLang Diffusion.
Ant inclusionAI has launched the sglang_ling_v3 repository on GitHub, whose contents are based on the open-source inference framework SGLang. The repository's README introduces that SGLang targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The README also shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes a built-in image and video generation engine, SGLang Diffusion. The repository has currently been released, with no further development activity yet.