@TensordyneInc: https://x.com/TensordyneInc/status/2066567307984531834
Summary
Tensordyne introduces Napier, an inference system using logarithmic math on silicon, claiming massive efficiency gains for MoE and reasoning models, with air-cooled racks.
View Cached Full Text
Cached at: 06/16/26, 09:39 PM
The AI speed vs. cost trade-off is officially over
Right now, builders are forced to choose between ultra-fast inference and actual profitability.
We built a system that delivers both.
Introducing **Tensordyne Napier, **the world’s first inference system powered by logarithmic math embedded directly onto silicon. By re-engineering AI compute from the math up, we’ve changed the industry standards for speed and efficiency.
What a single Tensordyne rack delivers:
-
Massive 2T MoE Models: Run 1,000 tokens/sec on just 1 single rack drawing 120 kW (compared to 9–14 racks of legacy infrastructure drawing up to 1.5MW).
-
Next-Gen Reasoning (DeepSeek-R1): Delivers 13x more tokens/sec and 17x more tokens/MW than an Nvidia NVL72 GB300.
-
Seamless Deployment: Packs 608 PFLOPS of FP8 compute but stays 100% air-cooled. Fits into your existing standard racks with no data center renovations.
-
The Bottom Line: Generates up to $33M more in annual revenue per rack compared to traditional alternatives.
Fast, low-cost AI has arrived.
We are ramping up production toward the end of the year.
For more information visit www.tensordyne.ai
2:13
Similar Articles
@rohanpaul_ai: Quite a massive inferencing rack breakthrough from @TensordyneInc . They just announced an AI-inference rack, claiming …
Tensordyne announces the Napier AI inference rack, claiming 13x the throughput of Nvidia's NVL72 GB300 by using log-space math to reduce energy and transistor usage, potentially disrupting the inference hardware landscape.
Tensordyne announces Logarithmic AI compute chips. 17x more tokens per watt and 13x higher throughput than NVIDIA Blackwell.
Tensordyne announced a breakthrough inference system using logarithmic math in hardware, claiming 17x more tokens per watt and 13x higher throughput than NVIDIA Blackwell, achieved by replacing complex multiplication with simple addition in log space.
@LinQingV: When exploring LLM inference chip architectures previously, I reviewed the architectures of the four major AI inference ASIC companies: Groq, SambaNova, Tenstorrent, and Cerebras. While the first three have different emphases, their underlying logic falls within the same framework: large on-chip SRAM + dataflow architecture + deterministic scheduling...
The article analyzes the AI inference ASIC architectures of Groq, SambaNova, Tenstorrent, and Cerebras, highlighting Cerebras's unique wafer-scale engine design. It discusses the benefits of deterministic latency and high bandwidth for LLM inference, while noting challenges like yield, cost, and KV cache bottlenecks.
@HotAisle: This is awesome. I wonder who's MI300x they used... ;-)
Kog announces real-time LLM inference achieving 3000+ output tokens per second per request on standard datacenter GPUs, bringing high-speed inference previously limited to custom silicon to production hardware.
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 · Hugging Face
NVIDIA releases Nemotron-3-Ultra-550B-A55B, a 550B parameter (55B active) frontier LLM featuring a hybrid LatentMoE architecture combining Mamba-2, MoE, and Attention layers, with up to 1M token context length and configurable reasoning mode. It supports 11 languages and is optimized for complex agentic workflows, long-context analysis, and high-accuracy reasoning.