Meta MTIA 450
MTIA 450 doubles MTIA 400's HBM bandwidth to 18.4 TB/s and lifts MX4 compute 75% for generative AI inference. Meta schedules it for mass deployment in early 2027. Sources and verification dates.
Every published figure
Source per value- Owner The company that specified the chip and runs it in its own cloud, data centers or products.
- Meta 1
- Role What the owner says the chip is for: training, inference, both, or a general-purpose server CPU.
- Inference 1
- Design partner The ASIC house that co-designed or physically implemented the chip (Broadcom, Marvell, MediaTek, Alchip, GUC and others), as stated by the owner, the partner, a filing or named trade-press reporting. 'In-house' when the owner states it designed the chip itself, including through an acquired team such as Annapurna Labs.
- Broadcom 1
- Partner basis Who says so: the owner, the partner, or press reporting with the outlet named. A reported partner is not confirmed by the owner.
- Stated by Meta: developed in close partnership with Broadcom. 1
- Memory On-package memory type (HBM2e, HBM3, HBM3E, LPDDR5) or, for CPUs, the memory the socket drives (DDR5).
- HBM 1
- Memory capacity GB On-package memory per chip in GB. Blank for CPUs, whose memory sits off-package.
- 288 1
- Memory bandwidth TB/s Peak memory bandwidth per chip in TB/s, as published.
- 18.4 1
- BF16 TFLOPS Published BF16 throughput per chip or accelerator module in TFLOPS. FP16 is a separate column. See the precision note for sparsity and module scope.
- 3,500 1
- MX8 TFLOPS Published MX8 throughput per chip or accelerator module in TFLOPS. See the precision note for shared-format figures, sparsity and module scope.
- 7,000 1
- MX4 TFLOPS Published MX4 throughput per chip or accelerator module in TFLOPS. See the precision note for shared-format figures, sparsity and module scope.
- 21,000 1
- Precision note What the throughput figures mean: dense or sparse, which precision, per chip or per package, and any figure the owner states only in another unit.
- Meta's chip-specifications image: 3500 BF16 TFLOPS, 7000 TFLOPS in its shared FP8/MX8 row, 21000 MX4 TFLOPS, per accelerator module. The shared FP8/MX8 figure is kept in the MX8 column and labelled here; Meta does not label sparsity. Module TDP 1400 W. 1
- Power W Chip or package power in watts as published (TDP, TBP or typical power; the source says which).
- 1,400 1
- Largest pod chips The largest scale-up domain the owner states: chips per pod, UltraServer, supernode or rack joined by the chip-to-chip interconnect.
- 72 1
- Status Announced: public roadmap without verified deployment. Sampling: first silicon or limited internal use. Preview: customers can request early access, without a GA claim. Deployed: stated production use or general availability. Discontinued: program stopped.
- Announced 1
- Announced Year the owner first announced the chip publicly.
- 2,026 1
- Deployment scale The largest deployment the owner or a named report states: chip counts, clusters, gigawatts or regions.
- Scheduled for mass deployment in early 2027 (Meta) 1
- How to get it How an outside buyer can use the chip: a named cloud instance or service, a product, or 'Internal only'.
- Internal only 1
Not verified in the sources reviewed: foundry, node, fp16, mxfp8, mxfp4, fp8, int8, fp4.
Sources
- Meta AI: MTIA: scaling AI chips for billions accessed 2026-11-11
- ServeTheHome: Meta outlines new MTIA accelerator roadmap for its next-gen AI compute mix accessed 2026-11-11