Every AI accelerator's manufacturing cost can be decomposed into four major layers: logic die fabrication, HBM memory, advanced packaging, and assembly/test. This AI chip cost breakdown comparison reveals how design decisions, supplier relationships, and technology choices drive dramatically different cost structures across competing chips.

The Four Cost Layers

The logic die cost depends on process node, die area, and wafer yield. A large monolithic die on TSMC 4N (like the H100) costs $250–350 in wafer cost alone, while a chiplet approach (like MI300X with its multi-die design) can improve yield at the expense of more complex packaging. HBM memory has become the dominant cost component for many AI chips—6–8 stacks of HBM3E can add $700–$1,500 to the GPU manufacturing cost. Packaging (CoWoS, EMIB, or organic substrate) adds $500–$1,500+, and test/assembly adds $100–$500.

Memory as the Dominant Cost Driver

For the latest generation of AI accelerators, HBM memory often represents 40–50% of total manufacturing cost. This is a structural shift from earlier GPU generations where the logic die was the primary cost center. The chip BOM analysis in this tool shows this clearly: compare the H100 (5 HBM3 stacks) against the B200 (8 HBM3E stacks) and you can see how memory cost scales with capacity and generation.

Comparing Design Strategies Through Cost Bridges

Cost bridge charts are powerful because they reveal strategic differences between vendors. NVIDIA's approach prioritizes maximum performance with premium packaging (CoWoS-L for B200). AMD's MI300X uses a multi-die chiplet design that trades packaging complexity for better logic die yields. Google's TPU v5p optimizes for internal workloads with a more balanced cost profile. By comparing these bridges side by side, procurement teams can understand what they're paying for and where negotiation leverage exists.

Related: Chip Price Calculator · Packaging Cost Model · Price/Performance Frontier