Captive silicon. MTIA is designed by Meta for its own data centers and cannot be bought or rented from anyone, and no cloud provider offers it. Unlike Google TPU or AWS Trainium there is no external access path of any kind, so there is no rental price for this part. MTIA 300 is in production today, powering ranking and recommendation training inside Meta.
ME

Meta MTIA 300 NEW

MTIA 2026
VRAM
216 GB
HBM
TDP
800 W
Bandwidth
6.1 TB/s
memory

Overview

The Meta MTIA 300 is the third generation of Meta's in-house Training and Inference Accelerator and the first of four chips Meta detailed in March 2026. It was designed for ranking and recommendation workloads, the dominant Meta workload before generative AI took off, and it is in production for R&R training. It pairs 216 GB of HBM at 6.1 TB/s with a module power budget of 800 W, and delivers 1.2 PFLOPS of FP8 and 0.6 PFLOPS of BF16. It has no MX4 datapath, which is the main capability gap against its successors. The chip is built from one compute chiplet and two network chiplets alongside several HBM stacks, with the compute chiplet holding a grid of processing elements, each containing two RISC-V vector cores, a Dot Product Engine for matrix multiplication, a Special Function Unit, a Reduction Engine and a DMA engine. Its distinguishing features against earlier MTIA parts are built-in NIC chiplets, dedicated message engines that offload communication collectives, and near-memory compute for reduction-based collectives. Those low-latency communication blocks became the foundation for the GenAI-focused chips that followed. Its scale-up domain is 16 accelerators, smaller than the 72 of later parts, which is why Meta gives it a faster 200 GB/s scale-out network to compensate.

Performance Metrics

Peak theoretical throughput by precision type

PrecisionBitsPeak TFLOPSEfficiency
FP8 8 1200.0 1.500 TFLOPS/W
BF16 16 600.0 0.750 TFLOPS/W
FP8 Efficiency
1.500 TFLOPS/W
1200.0 TFLOPS / 800W

Power Specifications

TDP

800 W

Max Power

920 W

Power Connector

PCIe 16-pin

Cooling

Air

Memory Specifications

Capacity

216 GB

Type

HBM

Bandwidth

6100 GB/s

Interface

--

Hardware & Design

Form Factor

--

Architecture

MTIA

Process Node

--

Launch Year

2026

Variant

Standard

Market Segment

Professional

Chiplets

3 (1 compute chiplet, 2 network chiplets, several HBM stacks)

Full Specifications

Chip Design
Chiplets 3 (1 compute chiplet, 2 network chiplets, several HBM stacks)
Memory
VRAM 216 GB
Memory Type HBM
Bandwidth 6.1 TB/s
Interconnect & I/O
Interconnect Bandwidth 2.0 TB/s
Power & Thermal
TDP 800 W
Enterprise Features
Compute APIs PyTorch, Triton
General
Architecture MTIA
Launch Year 2026

Documentation & Resources

Common Use Cases

General Compute AI/ML Workloads Data Processing

The Meta MTIA 300 is optimized for high-performance computing tasks with MTIA architecture delivering high TFLOPS of compute power.

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