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来源: 于 2025-09-29 22:15:02 [旧帖] [给我悄悄话] 本文已被阅读：次

Here are some of the advantages Broadcom’s ASICs have vs Nvidia’s GPUs in the context of AI reference (inference + reasoning) workloads — where the aim is to deploy AI models to answer or reason, not just train them. These aren’t absolute; there are trade-offs. But in many use cases, Broadcom’s ASIC-based approach can pull ahead in certain dimensions.

What is an ASIC vs GPU, in this context

  • GPU (Graphics Processing Unit): A general-purpose accelerator with lots of parallelism, often programmable. Nvidia’s GPUs are very good at training large models, doing many different operations, and benefit from mature software stacks (CUDA etc.).

  • ASIC (Application Specific Integrated Circuit): Custom hardware built to perform specific tasks (or classes of tasks) very efficiently. Less flexible, but much more optimized for the operations the developer cares about.

Key advantages of Broadcom’s ASICs for reasoning/inference workloads

  1. Higher efficiency (performance per watt / per area / cost)
    ASICs can often be optimized to do exactly what is needed — perhaps large-matrix multiply, quantized math, fixed kernels, inference paths — without many of the overheads that a GPU generality requires. This lets them achieve:

    • Lower power consumption per inference. (ASICs avoid overheads like general control logic, wide floating-point support if not needed, etc.)

    • Lower latency, especially for inference. Tasks like reasoning often benefit from lower latency (responding to queries fast) more than raw peak throughput.

    That means lower operating costs (power, cooling, etc.), which scale up a lot in large data center or deployment contexts. (The Motley Fool)

  2. Cost effectiveness / total cost of ownership (TCO)
    Once you build out ASICs for inference, and if the workload is stable (same kinds of operations repeatedly), you can amortize the upfront design and production cost over many inference instances. Broadcom is lining up large hyperscaler customers to deploy many units, so that amortization makes sense. (The Motley Fool)

  3. Optimization for inference and reasoning use-cases
    Many reasoning/ref­er­ence tasks are about latency, predictability, efficiently moving data (memory access patterns), and maybe using quantized math or mixed-precision arithmetic. ASICs can be tuned for those specific operations. GPUs are more flexible, but that flexibility incurs overhead. Broadcom’s ASICs are used by large cloud providers (e.g. Google with its TPUs / Trillium) to accelerate inference. (Nasdaq)

  4. Better scaling of inference load in hyperscaler / data center environments
    When you have many instances of inference/serving queries (for example, many users asking LLMs questions), the cost per query matters a lot. ASICs optimized for interconnect, efficient memory access, and power can reduce cost significantly when scaled. Broadcom also emphasizes networking ASICs (interconnect) to tie chips efficiently, which helps scale out these deployments. (Reuters)

  5. Lower latency and more predictable performance
    Because ASICs are built for fixed operations, they can minimize or eliminate certain sources of overhead or variability that general-purpose GPUs must manage. This can lead to more consistent and lower latency, which is especially useful in reasoning / conversational tasks. GPUs may have more overhead in scheduling, memory transfers, context switching, etc.

  6. Power / efficiency trade-off particularly favorable for inference vs training
    GPUs are often optimized for throughput (many operations in parallel), which is critical for training. But inference often has different constraints: many smaller operations, need for fast responses, energy constraints. ASICs tend to dominate here. Broadcom’s reports indicate substantial improvements in power/performance for inference tasks. (www.alphaspread.com)

What are the trade-offs / where GPUs may still be better

To be fair, there are reasons many systems still use, or start with, GPUs — especially in the reasoning / reference domain, depending on what exactly needs to be done.

  • Flexibility: If the model architecture, precision, or inference workflow changes, ASICs are less adaptable. GPUs are general purpose; you can run many different models, experiment, change quantization, etc., without changing hardware.

  • Time to market / design cost: Designing custom ASICs takes time, risk, and significant upfront investment. For rapidly evolving models or for smaller deployments, that may not pay off.

  • Ecosystem / software stack: Nvidia’s CUDA, libraries, tooling, etc., are very mature. Many AI frameworks assume GPU execution; switching to ASIC may require rewriting kernels, custom software, etc.

  • Training performance: If you also want to do training, GPUs tend to still lead in generality and in many parts of the training workflow (backprop, high precision, etc.).

  • Volume, scale, customer base: For very large scale, stable inference workloads, ASICs scale well. But many workloads are mixed or evolving; GPUs help cover unknowns.

Summary

In sum: Broadcom’s ASICs, when applied to reference / reasoning / inference workloads, tend to offer better efficiency, lower power cost, lower latency, and better cost per inference when the workload is known and stable. That gives them advantages at scale in large data centers or with hyperscalers. GPUs remain more flexible and better for experimentation, training, model changes, or mixed workloads.

If you like, I can compare specific numbers (e.g. energy per inference, latency, cost) for Broadcom’s ASICs vs Nvidia GPUs in reasoning tasks, to see how large the advantage tends to be in practice. Do you want me to pull those?