{"product_id":"tenstorrent-blackhole-p150a-32gb","title":"Tenstorrent Blackhole p150a (32GB)","description":"\u003cp\u003eThe Tenstorrent Blackhole p150a is Tenstorrent’s newest and most powerful single-card AI accelerator: an actively cooled PCIe card built around the Blackhole architecture, the successor to Wormhole. It packs more Tensix cores, more on-chip SRAM and faster memory into a single-ASIC design, backed by a fully open software stack. It suits researchers, kernel engineers and AI developers who want serious on-card compute in a desktop- or workstation-ready package.\u003c\/p\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAI research teams building or fine-tuning custom model architectures\u003c\/li\u003e\n\u003cli\u003eKernel engineers who want to hand-optimise compute graphs at a level GPU vendors do not expose\u003c\/li\u003e\n\u003cli\u003eRISC-V-focused developers who want general-purpose cores sitting beside the AI compute fabric\u003c\/li\u003e\n\u003cli\u003eTeams planning a multi-card cluster that scales bandwidth beyond standard PCIe-only multi-GPU setups\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKey features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e120 Tensix cores:\u003c\/strong\u003e Tenstorrent’s core compute unit, each built around a cluster of small RISC-V processors rather than a traditional GPU shader pipeline, for fine-grained control over how work is scheduled and executed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e16 “big” RISC-V cores:\u003c\/strong\u003e general-purpose processors on the die for control, orchestration and host-side logic, so latency-sensitive or highly custom pipelines make fewer round-trips to the CPU.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e180MB of on-chip SRAM:\u003c\/strong\u003e roughly 1.5MB per Tensix core, a large local pool that keeps data close to the compute units and eases pressure on off-chip DRAM bandwidth.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e32GB GDDR6 on a 256-bit bus:\u003c\/strong\u003e 16GT\/sec for 512GB\/s of memory bandwidth, enough room for meaningfully large local models and datasets.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUp to 1.35GHz AI clock:\u003c\/strong\u003e delivers 664 TeraFLOPS at BLOCKFP8 precision, a substantial step up from Wormhole.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e4x QSFP-DD 800G ports:\u003c\/strong\u003e passive high-speed networking that links multiple Blackhole cards to pool memory and scale compute across a multi-card system.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePCIe 5.0 x16 host interface:\u003c\/strong\u003e the latest-generation host interconnect for maximum throughput between card and system.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFully open-source software stack:\u003c\/strong\u003e the open compiler and runtime approach (TT-Metalium, TT-NN) that defines Tenstorrent’s product line, with nothing hidden behind proprietary drivers.\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\u003eTensix cores\u003c\/td\u003e\n\u003ctd\u003e120\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRISC-V cores\u003c\/td\u003e\n\u003ctd\u003e16 (“big” RISC-V, on-die)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOn-chip SRAM\u003c\/td\u003e\n\u003ctd\u003e180MB (~1.5MB per Tensix core)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e32GB GDDR6, 256-bit bus @ 16GT\/sec\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd\u003e512GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI clock\u003c\/td\u003e\n\u003ctd\u003eUp to 1.35GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute performance\u003c\/td\u003e\n\u003ctd\u003e664 TeraFLOPS (BLOCKFP8)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower\u003c\/td\u003e\n\u003ctd\u003e300W total board power, 1x 12+4-pin (12V-2×6) connector\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNetworking\u003c\/td\u003e\n\u003ctd\u003e4x QSFP-DD 800G (passive) for multi-card scale-out\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e42mm x 270mm x 111mm, actively cooled\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\u003eThe trade-off is software maturity versus openness. TT-Metalium and TT-NN are open, actively developed and show how your workload maps to silicon.\u003c\/li\u003e\n\u003cli\u003eThe ecosystem of pre-optimised model support is smaller than CUDA’s or even ROCm’s. Expect to work with Tenstorrent’s programming model directly rather than getting instant, zero-effort parity with a mainstream GPU framework.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChoose the p150a if you value an open stack you can read and modify at every layer and are willing to invest in learning it. If you want mainstream frameworks to work with no effort, a conventional GPU is the easier route.\u003c\/p\u003e","brand":"Tenstorrent","offers":[{"title":"Default Title","offer_id":58232538104190,"sku":"TT-BLACKHOLE-P150A","price":1399.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1022\/4468\/0062\/files\/ai-blackhole-p150_589b92da-0b08-4c15-9da9-37c1c6bd6696.png?v=1792094044","url":"https:\/\/computahardware.net\/products\/tenstorrent-blackhole-p150a-32gb","provider":"ComputaHardware","version":"1.0","type":"link"}