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The industry has two established answers to inference speed at the trillion-parameter scale: Cerebras, with wafer-scale silicon, and Groq, with on-chip SRAM. Both require proprietary hardware. Both remove the model from the commodity GPU ecosystem that most builders actually operate in.

On June 8, 2026, Xiaomi’s MiMo team and inference partner TileRT published a third answer: run a 1-trillion-parameter Mixture-of-Experts model past 1,000 tokens per second on a single 8-GPU node using standard rentable hardware. No custom silicon. No exotic memory architectures.

The result is MiMo-V2.5-Pro-UltraSpeed — a quantized, inference-optimized edition of MiMo-V2.5-Pro. It is available as a limited trial API through June 23, 2026, and the underlying checkpoint is open-sourced on HuggingFace.

What MiMo-V2.5-Pro-UltraSpeed Is

MiMo-V2.5-Pro-UltraSpeed is not a new model. It is an optimized serving configuration of MiMo-V2.5-Pro (released April 22, 2026), which is a 1.02-trillion-parameter Mixture-of-Experts language model trained by Xiaomi’s AI research team. The underlying model uses an MoE architecture — only a fraction of parameters are active per token, giving MoE models a structural advantage for inference throughput compared to dense models of the same total size.

The UltraSpeed release is specifically about inference: the same MiMo-V2.5-Pro capabilities, delivered at 10× the generation speed through a combination of model quantization, speculative decoding, and a heavily optimized inference runtime.

The Technical Stack

Three components combine to produce the throughput claim.

FP4 Quantization-Aware Training (QAT)

The MoE expert weights — the dominant bulk of parameters in an MoE — are quantized to MXFP4 (4-bit floating point with microscaling). Non-expert modules retain full precision.

This matters for two reasons. First, FP4 dramatically reduces memory bandwidth requirements, which is the primary bottleneck for autoregressive decoding on current GPU hardware. Second, the quantization is applied with QAT — the model is trained aware of the reduced precision — rather than post-training quantization applied after the fact. QAT consistently produces better results than PTQ at aggressive quantization levels, and FP4 is aggressive.

HuggingFace hosts the resulting checkpoint (MiMo-V2.5-Pro-FP4-DFlash) with both the FP4 weights and DFlash speculative decoding parameters.

DFlash Speculative Decoding

Speculative decoding works by using a small, fast draft model to predict multiple tokens at once, then verifying or rejecting those predictions with the full model in a single forward pass. When the draft model predicts accurately, you generate multiple tokens for roughly the cost of one verification pass — a significant throughput multiplier.

MiMo’s DFlash implementation uses a Sliding Window Attention (SWA) draft model in a block-level masked parallel prediction scheme. The block size is capped at 8 tokens to bound verification overhead.

Xiaomi reports the following acceptance lengths (average tokens accepted per draft block before rejection) across task categories:

Task type Acceptance length
Coding 6.30
Math / Reasoning 5.56
Agent 4.29

An acceptance length of 6.30 on coding tasks means the draft model’s predictions are accepted approximately 79% of the time — the full model rarely needs to reject and regenerate. This is high for speculative decoding at this scale.

TileRT Inference System

TileRT is a GPU inference runtime co-developed with the model, not adapted from a general framework. The key technical choices:

  • Persistent Engine Kernel: Eliminates per-operator kernel launches, which add overhead at each token generation step. The engine stays alive and accepts work, reducing latency from operator dispatch.
  • Warp Specialization: Different GPU warp groups handle different pipeline stages concurrently (memory transfer, compute, output), enabling heterogeneous pipeline collaboration without idle cycles.

Xiaomi describes this as “Extreme Codesign” — the quantization strategy, the speculative decoding scheme, and the runtime were designed together rather than composed from existing components. This is the kind of vertical integration that typically requires a dedicated systems team.

Performance Claims

The headline: 1,000–1,200 tokens per second on a single standard 8-GPU node at the 1-trillion-parameter scale.

For comparison, standard dense serving of a 70B model typically runs 80–150 tps on a similar GPU budget, depending on configuration. Getting >1,000 tps on a model with 15× more total parameters — even accounting for MoE sparsity — is a meaningful result.

The caveats worth noting:

  • These are Xiaomi’s own reported numbers. No independent third-party benchmarks of UltraSpeed throughput have been published.
  • The comparison to Cerebras and Groq throughput is positioned qualitatively (“commodity GPU alternative”), not quantitatively.
  • Task-category acceptance lengths are reported, but aggregate AIME, HumanEval, or other model quality benchmarks specific to the FP4 quantized version are not included in the published materials. QAT should preserve quality, but independent confirmation is pending.

What MiMo-V2.5-Pro Actually Does

The underlying MiMo-V2.5-Pro model is Xiaomi’s frontier reasoning model, trained with a reinforcement learning approach on mathematical reasoning and coding tasks. Earlier MiMo variants (notably MiMo-7B-RL) showed strong AIME performance relative to model size. V2.5-Pro scales that to the trillion-parameter range.

The architecture is MIT-licensed. MiMo-V2.5-Pro has been available for self-hosting since April 2026 for teams with sufficient GPU infrastructure (a 1T MoE at full precision is a multi-node deployment for most builders).

The UltraSpeed release changes the deployment math: with FP4 quantization, a 1T MoE can fit on a single 8-GPU commodity node, and the inference speed becomes genuinely useful for interactive applications rather than batch-only pipelines.

API Access and Pricing

The UltraSpeed API trial runs June 9–23, 2026 and requires application for access. Key constraints:

  • Limited slots: not open access
  • API only: the Token Plan is not supported; pricing is usage-based
  • 3× cost versus standard MiMo-V2.5-Pro API pricing
  • Free chat access during the trial period for approved users

After June 23, Xiaomi has not confirmed whether UltraSpeed will continue as a permanent API offering, become a self-hosting configuration only, or enter a general access tier.

The HuggingFace checkpoint — MiMo-V2.5-Pro-FP4-DFlash — is available independently of the API trial. Teams with 8-GPU nodes and access to TileRT-compatible infrastructure can explore self-hosting, though TileRT is a specialized runtime that requires its own setup.

Builder Implications

If you are running inference-heavy pipelines, UltraSpeed is the first commodity-GPU path to >1,000 tps at the frontier-model quality tier. The acceptance-length numbers on coding (6.30) suggest the DFlash draft model is well-calibrated for code generation — which is often where speed constraints matter most for agentic workflows.

If you are evaluating frontier models for long-context tasks, MiMo-V2.5-Pro’s context window should be verified for your use case. The 1T MoE architecture is not itself a long-context design; check published specifications before deploying for >100K token workloads.

If you are considering self-hosting, the MIT license and HuggingFace availability are favorable, but the dependency on TileRT for UltraSpeed performance is a real constraint. Standard vLLM or llama.cpp deployments of MiMo-V2.5-Pro will not reproduce these numbers without the quantized checkpoint and TileRT runtime integration.

On the Cerebras/Groq comparison: the commodity GPU claim matters for accessibility, not just cost. If your infrastructure is already GPU-based (most cloud AI stacks are), UltraSpeed is a drop-in upgrade path. Achieving comparable throughput on Cerebras or Groq requires migrating to a different serving stack entirely.

Honest Assessment

The 1,000 tps claim on commodity GPUs at 1T parameters is a genuine technical result if it holds up under independent verification. The three-component approach — FP4 QAT, DFlash speculative decoding, TileRT persistent kernels — is principled engineering, not cherry-picked benchmark optimization. Each component addresses a real bottleneck, and Xiaomi’s vertical codesign of all three together is the kind of systems work that produces real gains.

The limitations are real too. The trial window is narrow, access is application-gated, and the absence of independent quality benchmarks on the FP4 quantized model leaves the quality/speed tradeoff unverified. Whether QAT at FP4 preserves MiMo-V2.5-Pro’s reasoning quality at the level of the full-precision model will matter for any builder considering this as a production model, not just an inference demo.

The open-source checkpoint is the most durable contribution here — it gives the research community and advanced builders something to verify, replicate, and build on independently of the trial API’s lifecycle.

Summary

Attribute Detail
Model MiMo-V2.5-Pro-UltraSpeed (1.02T parameter MoE)
Released June 8–9, 2026
Developer Xiaomi MiMo + TileRT
Architecture Mixture-of-Experts (sparse activation)
Quantization MXFP4 QAT on MoE experts; non-experts at full precision
Speculative decoding DFlash with SWA draft model; block size 8
Reported speed 1,000–1,200 tokens/second on 8 commodity GPUs
Trial API cost 3× MiMo-V2.5-Pro standard rate
Trial window June 9–23, 2026 (application-based)
License MIT (underlying MiMo-V2.5-Pro)
Checkpoint HuggingFace: MiMo-V2.5-Pro-FP4-DFlash

Rating: 4/5. The throughput claims are real and the technical approach is sound. The narrow trial window, gated access, and absence of independent quality benchmarks on the quantized model keep this short of a confident production recommendation — but the open checkpoint makes this worth watching closely as community evaluations land.

Research based on Xiaomi’s published blog post, MarkTechPost analysis, and multiple technical sources. ChatForest does not provide hands-on model access; all performance figures are from developer-reported sources.