{"product_id":"tenstorrent-wormhole-n300-24gb","title":"Tenstorrent Wormhole n300 (24GB)","description":"\u003cp\u003eThe Tenstorrent Wormhole n300 is a purpose-built AI accelerator, designed from the ground up by Jim Keller’s Tenstorrent around a fully open-source software stack. Each card carries a pair of Wormhole Tensix processors on a single PCIe board. It suits researchers, AI engineers and hardware enthusiasts who want full visibility into the compiler, the kernel implementations and the hardware itself.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eLocal LLM inference on quantized open-weight models\u003c\/li\u003e\n\u003cli\u003eMultichip AI research that needs to scale beyond a single card’s memory through mesh networking\u003c\/li\u003e\n\u003cli\u003eTeams that want to develop and profile custom kernels rather than rely on vendor-tuned libraries\u003c\/li\u003e\n\u003cli\u003eOpen-hardware researchers and RISC-V enthusiasts building their own inference and training pipelines\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDual Wormhole Tensix processors:\u003c\/strong\u003e two full ASICs on one card, totalling 128 Tensix cores (64 per processor), each core built around an array of small RISC-V control processors rather than a fixed-function shader pipeline.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e24GB GDDR6 memory:\u003c\/strong\u003e runs at 12GT\/sec for 576GB\/s of memory bandwidth, enough to hold meaningfully large quantized models for local inference and experimentation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e192MB of on-chip SRAM:\u003c\/strong\u003e 96MB per ASIC, a large, fast local pool that Tensix cores lean on to reduce off-chip memory pressure, unlike a traditional GPU cache hierarchy.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFully open-source software stack:\u003c\/strong\u003e the compiler and runtime (TT-Metalium, TT-NN) are open source on GitHub, with ISA documentation and low-level access most GPU vendors never expose, so you can hand-tune kernels for your exact workload.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNative multichip mesh networking:\u003c\/strong\u003e 2x QSFP-DD 400GbE ports plus 2x Warp 100 bridge connectors link multiple Wormhole cards into larger mesh configurations, scaling compute and effective memory.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e1GHz AI clock:\u003c\/strong\u003e delivers up to 466 TFLOPS FP8, 262 TFLOPS BFP8 and 131 TFLOPS FP16 across the card.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe 4.0 x16 host interface:\u003c\/strong\u003e a standard interface for any modern workstation or server motherboard.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eSpec\u003c\/th\u003e\n\u003cth\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eProcessors\u003c\/td\u003e\n\u003ctd\u003e2x Wormhole Tensix Processor (128 Tensix cores total, 64 per ASIC)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOn-chip SRAM\u003c\/td\u003e\n\u003ctd\u003e192MB (96MB per ASIC)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6 @ 12GT\/sec\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e576GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI clock\u003c\/td\u003e\n\u003ctd\u003e1GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e466 TFLOPS FP8 \/ 262 TFLOPS BFP8 \/ 131 TFLOPS FP16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower\u003c\/td\u003e\n\u003ctd\u003eUp to 300W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNetworking\u003c\/td\u003e\n\u003ctd\u003e2x QSFP-DD 400GbE + 2x Warp 100 bridge for multichip mesh scaling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm factor\u003c\/td\u003e\n\u003ctd\u003eActively cooled PCIe card\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware\u003c\/td\u003e\n\u003ctd\u003eFully open-source stack (TT-Metalium, TT-NN)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eGood to know\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIt is not a drop-in replacement for a mature CUDA or ROCm stack running mainstream, off-the-shelf inference frameworks.\u003c\/li\u003e\n\u003cli\u003eTenstorrent’s ecosystem is capable and improving quickly, but it has a smaller library of pre-optimised model support and a steeper learning curve than the incumbent GPU vendors.\u003c\/li\u003e\n\u003cli\u003eExpect to engage with the software stack directly rather than installing a framework with pip and getting instant parity with an NVIDIA card.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the Wormhole n300 if open hardware and software, multichip scaling and kernel-level control matter more to you than out-of-the-box framework support. If you just want mainstream tools to run, pick a conventional GPU.\u003c\/p\u003e","brand":"Tenstorrent","offers":[{"title":"Default Title","offer_id":58232538071422,"sku":"TT-WORMHOLE-N300","price":1399.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/ai-wormhole-n300-scaled_7db1e3dc-2c7c-4af3-9389-8fc66502ef24.png?v=1792094043","url":"https:\/\/computahardware.net\/products\/tenstorrent-wormhole-n300-24gb","provider":"ComputaHardware","version":"1.0","type":"link"}