MT65B12G16080A00QG-92:A - 24GB HBM3E 2.3GHz X1088 WFPGA | Micron
N.º da peça: MT65B12G16080A00QG-92:A ✓ Ativo| Qtd. | Preço unitário | Total |
|---|---|---|
| 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 Visão geral
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.
Alternativas drop-in para MT65B12G16080A00QG-92:A — mesmo encapsulamento, compatível pino a pino. Peças de encapsulamento diferente que exigem retrabalho na PCB são excluídas.
Ferramenta de comparação rápida — selecione peças alternativas para comparação lado a lado:
MT65B12G16080A00QG-92:A.TR
✅ Drop-In📋 Alternativa de referência (fora do catálogo)
MT65B12G16080A00QG-92:B
✅ Drop-In📋 Alternativa de referência (fora do catálogo)
MT65B12G16080A00QG-60:A
✅ Drop-In📋 Alternativa de referência (fora do catálogo)
MT65B12G16080A00QG-60:B
✅ Drop-In📋 Alternativa de referência (fora do catálogo)
MT65B12G16080A00QG-92:A Classificações máximas e características elétricas
| Tipo de memória | HBM3E (High Bandwidth Memory 3E) |
| Density per Stack | 24 GB |
| Organização da memória | X1088 |
| Taxa de dados | 2.3 GHz |
| Interface Width per Stack | 1024-bit (HBM3E standard) |
| Number of Channels per Stack | 16 channels (HBM3E standard) |
| Encapsulamento | WFPGA (wafer-level stacked package) |
| 3D Stack Architecture | Multi-die DRAM stack with TSV on base logic die |
| JEDEC Standard | JEDEC HBM3E |
| Status RoHS | Compliant (per Micron product page) |
| Lead-Free | Yes |
| Tipo de montagem | Surface Mount (interposer / BGA attach) |
| Application | AI/ML training & inference, HPC, GPU memory |
Guia de Configuração de Pinos do MT65B12G16080A00QG-92:A wfpga (wafer-level stacked package)
Configuração de pinos para MT65B12G16080A00QG-92:A (pacote wfpga (wafer-level stacked package)). Este CI digital inclui GPIO, interfaces de comunicação (UART, SPI, I2C) e pinos de alimentação. Consulte o datasheet do fabricante para funções alternativas dos pinos e opções de configuração. Essencial para projeto de sistemas embarcados e layout de PCB.
Nenhum dado detalhado de pinagem disponível para MT65B12G16080A00QG-92:A.
Consulte a ficha técnica para a configuração completa dos pinos.
Aplicações típicas
MT65B12G16080A00QG-92:A é adequado para 6 aplicaçãos: 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.
Recomendado
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.
Recomendado
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.
Recomendado
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.
Recomendado
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.
Recomendado
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.
Recomendado
Resumo dos produtos recomendados
Dados de referência de engenharia para MT65B12G16080A00QG-92:A — comparação, orientação de projeto e informações de conformidade.
Guia de seleção
Comparação com alternativas
| Parâmetro | Este produto | 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) |
Principais diferenciais
- 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)
Notas de projeto
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.
Informações de conformidade
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.
