MT65B12G16080A00QG-92:A - 24GB HBM3E 2.3GHz X1088 WFPGA | Micron
부품 번호: MT65B12G16080A00QG-92:A ✓ 활성| 수량 | 단가 | 합계 |
|---|---|---|
| 1 | $1850 | $1,850.00 |
| 10 | $1720 | $17,200.00 |
| 100 | $1620 | $162,000.00 |
| 500 | $1550 | $775,000.00 |
| 1,000 | $1495 | $1,495,000.00 |
MT65B12G16080A00QG-92:A 개요
HBM (High Bandwidth Memory) is a JEDEC-standardized 3D-stacked DRAM architecture that places multiple DRAM dies atop a base logic die through TSV (Through-Silicon Via) interconnects. It belongs to the wider hierarchy: stacked DRAM -> graphics DRAM -> volatile memory -> semiconductor memory. Compared with conventional GDDR, HBM uses a wide 1024-bit per stack interface and a 3D package to deliver far higher bandwidth at lower power-per-bit, making it the preferred memory for AI training and inference engines.
Key specifications for this part include a 24 GB density per stack, an X1088 channel organization, a 2.3 GHz effective data rate, a wide I/O interface, and operation over the standard HBM3E voltage range. The WFPGA package integrates the stacked die and the base logic die into a single customer-facing footprint that mates to a host accelerator or GPU silicon through a fine-pitch BGA-style interconnect.
Architecturally, HBM3E increases per-pin data rates and tightens timing margins over HBM3, while keeping the same 1024-bit interface per stack and the same channel/row-bank structure. The device is used in multi-stack configurations (typically 4H, 6H, 8H, or 12H) to scale bandwidth up to roughly 1 TB/s and beyond, with independent channels allowing concurrent read/write across multiple processing units.
Typical applications include AI/ML training accelerators (NVIDIA H200-class GPUs, custom ASICs), high-end inference platforms, HPC simulation, and advanced graphics. The 24 GB density per stack enables very large model weights to be co-located with compute, reducing off-chip traffic. When designing with this device, follow JEDEC HBM3E thermal and signal-integrity guidelines, including controlled-impedance routing, decoupling, and a heatsink or cold-plate to manage the high sustained bandwidth power profile.
드롭인 대체품: MT65B12G16080A00QG-92:A — 동일 패키지, 핀 호환. PCB 재작업이 필요한 다른 패키지 부품은 제외됩니다.
빠른 비교 도구 — 나란히 비교할 대체 부품을 선택하십시오:
MT65B12G16080A00QG-92:A.TR
✅ 드롭인📋 참조 대체품 (카탈로그에 없음)
MT65B12G16080A00QG-92:B
✅ 드롭인📋 참조 대체품 (카탈로그에 없음)
MT65B12G16080A00QG-60:A
✅ 드롭인📋 참조 대체품 (카탈로그에 없음)
MT65B12G16080A00QG-60:B
✅ 드롭인📋 참조 대체품 (카탈로그에 없음)
MT65B12G16080A00QG-92:A 최대 정격 & 전기적 특성
| 메모리 유형 | HBM3E (High Bandwidth Memory 3E) |
| Density per Stack | 24 GB |
| 메모리 구성 | X1088 |
| 데이터 속도 | 2.3 GHz |
| Interface Width per Stack | 1024-bit (HBM3E standard) |
| Number of Channels per Stack | 16 channels (HBM3E standard) |
| 패키지 | WFPGA (wafer-level stacked package) |
| 3D Stack Architecture | Multi-die DRAM stack with TSV on base logic die |
| JEDEC Standard | JEDEC HBM3E |
| RoHS 상태 | Compliant (per Micron product page) |
| Lead-Free | Yes |
| 실장 유형 | Surface Mount (interposer / BGA attach) |
| Application | AI/ML training & inference, HPC, GPU memory |
MT65B12G16080A00QG-92:A wfpga (wafer-level stacked package) 핀 구성 가이드
MT65B12G16080A00QG-92:A(wfpga (wafer-level stacked package) 패키지)의 핀 구성입니다. 이 디지털 IC에는 GPIO, 통신 인터페이스(UART, SPI, I2C), 전원 핀이 포함되어 있습니다. 대체 핀 기능과 구성 옵션은 제조사 데이터시트를 참조하십시오. 임베디드 시스템 설계와 PCB 레이아웃에 필수적입니다.
다음에 대한 상세 핀아웃 데이터 없음: MT65B12G16080A00QG-92:A.
다음을 참조하십시오: 데이터시트 전체 핀 구성은 다음을 참조하십시오.
일반적인 애플리케이션
MT65B12G16080A00QG-92:A 적합 용도: 6 애플리케이션s: AI Training Accelerator Memory, GPU and Discrete Graphics Memory, High-Performance Computing (HPC) Nodes, Large Language Model Inference Platforms, AI Inference Edge Servers, Datacenter Memory Tier for Custom ASICs.
AI Training Accelerator Memory
The MT65B12G16080A00QG-92:A fits AI training accelerators as the primary stacked DRAM, where its 24 GB density and 2.3 GHz HBM3E data rate deliver the per-stack bandwidth needed to feed large parameter-server updates without off-chip traffic. Compared to GDDR6X, the 1024-bit interface and 3D-stacked WFPGA package lower power-per-bit and shorten on-chip routing. Designers typically deploy 4H to 8H stacks to reach 1+ TB/s aggregate bandwidth for transformer training.
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GPU and Discrete Graphics Memory
In high-end GPU and discrete graphics designs, the MT65B12G16080A00QG-92:A acts as the framebuffer-equivalent HBM3E stack. The 24 GB capacity supports high-resolution textures and large ray-tracing BVH structures, while the 2.3 GHz rate over 16 channels maintains frame throughput at 4K/8K. The WFPGA package mounts via silicon interposer to the GPU die, minimizing PHY-to-memory distance and reducing signal-integrity challenges.
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High-Performance Computing (HPC) Nodes
HPC nodes for simulation, scientific computing, and large-model serving deploy the MT65B12G16080A00QG-92:A to deliver sustained memory bandwidth for sparse-matrix and stencil kernels. The JEDEC HBM3E compliance and 1024-bit interface per stack simplify host ASIC integration, while the 24 GB capacity enables larger working sets per node. Thermal design must account for the bandwidth-dependent power envelope; cold-plate cooling is standard at the 2.3 GHz bin.
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Large Language Model Inference Platforms
LLM inference platforms benefit from the MT65B12G16080A00QG-92:A's 24 GB capacity, which holds substantial slices of multi-billion-parameter models in a single stack and reduces DRAM-to-compute hops. The 2.3 GHz HBM3E data rate sustains token-generation throughput at low latency. Multi-stack configurations (6H, 8H) scale capacity and bandwidth proportionally, fitting into accelerator ASICs validated against Micron's HBM3E PHY.
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AI Inference Edge Servers
For edge-server AI inference, the MT65B12G16080A00QG-92:A offers a 24 GB HBM3E stack in WFPGA that pairs with inference ASICs needing more bandwidth than LPDDR5X can deliver. Designers may choose the lower-speed 1.5 GHz bin for power-constrained edge SKUs and still benefit from the JEDEC HBM3E wide interface. Mechanical integration follows standard HBM3E interposer guidelines from Micron's HBM3E datasheet.
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Datacenter Memory Tier for Custom ASICs
Custom AI ASICs in datacenter deployments adopt the MT65B12G16080A00QG-92:A as their main memory tier, leveraging the 2.3 GHz HBM3E bandwidth to feed thousands of MAC units per cycle. The WFPGA package integrates cleanly into 2.5D silicon-interposer substrates alongside the host die, while the JEDEC-compliant interface shortens validation cycles. Stack counts of 4H to 12H tune aggregate bandwidth and capacity to the ASIC's target workload.
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추천 제품 요약
엔지니어링 참조 데이터: MT65B12G16080A00QG-92:A — 비교, 설계 지침 및 규정 준수 정보.
선택 가이드
대체품과의 비교
| 파라미터 | 이 제품 | MT65B12G16080A00QG-92:A.TR | MT65B12G16080A00QG-92:B | MT65B12G16080A00QG-60:A | MT65B12G16080A00QG-60:B |
|---|---|---|---|---|---|
| Brand | Micron Technology | Micron Technology | Micron Technology | Micron Technology | Micron Technology |
| Package | WFPGA | WFPGA - same | WFPGA - same | WFPGA - same | WFPGA - same |
| Density per Stack | 24 GB | 24 GB | 24 GB | 24 GB | 24 GB |
| Data Rate | 2.3 GHz | 2.3 GHz | 2.3 GHz | 1.5 GHz (-35%) | 1.5 GHz (-35%) |
| Organization | X1088 | X1088 | X1088 | X1088 | X1088 |
| JEDEC Standard | HBM3E | HBM3E | HBM3E | HBM3E | HBM3E |
| Silicon Revision | A | A | B | A | B |
| Per-Stack Bandwidth Class | High (2.3 GHz) | High (2.3 GHz) | High (2.3 GHz) | Standard (1.5 GHz) | Standard (1.5 GHz) |
주요 차별점
- Higher per-stack data rate vs lower-speed HBM3E bin (vs MT65B12G16080A00QG-60:A)
- Drop-in compatible 2.3 GHz HBM3E alternative (vs MT65B12G16080A00QG-92:B)
- Traceability / packaging variant at same speed grade (vs MT65B12G16080A00QG-92:A.TR)
설계 노트
Estimated: at the 2.3 GHz HBM3E bin the MT65B12G16080A00QG-92:A sustains bandwidth-dependent power dissipation well above conventional DRAM. In a 4H configuration the HBM subsystem can draw 60-100 W under training workloads; specify a cold-plate or high-performance heatsink and ensure junction temperature stays within the datasheet envelope. Always use Micron's HBM3E thermal model in your system-level simulation.
The WFPGA package must be co-designed with the host ASIC/GPU on a silicon interposer or organic substrate; PCB-only fan-out is not supported at HBM3E speeds. Maintain controlled-impedance routing for the 1024-bit interface and follow Micron's HBM3E channel-mapping guide for proper byte-lane training. Place decoupling per Micron reference layout to limit supply noise on VDDC/VDDQ.
At 2.3 GHz the HBM3E interface is extremely sensitive to channel skew and reflections. Use Micron's IBIS models for the WFPGA package, simulate worst-case corners, and budget crosstalk across adjacent channels. The 16 independent channels must each complete training before host traffic begins; ensure your PHY supports Micron HBM3E training sequences.
Do not assume the -92 and -60 HBM3E speed bins are interchangeable on the same host design — the host PHY must be configured for the rated data rate and timing parameters. Mixing bins across a multi-stack configuration causes training failures and link instability. Also verify the silicon revision (A vs B) is on your accelerator's Micron HBM3E qualified-vendor list before committing to volume.
규정 준수 정보
RoHS compliance per Micron HBM3E product page; AEC-Q100 not applicable for stacked DRAM. REACH and halogen-free status not stated in the verified data — left as unknown.
