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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteShort answer: choose a Google TPU when your model is a dense, well-supported workload that maps cleanly to XLA or JAX, scales across a pod, and benchmarks at a lower cost per completed result. Choose an NVIDIA GPU when CUDA libraries, custom kernels, PyTorch compatibility, model variety, rapid experimentation, or portability matter more than maximum pod-scale efficiency. Neither accelerator is universally faster or cheaper: the decisive measurement is your exact model, precision, batch size, sequence length, parallelism, serving engine, and target region.
TPUs are Google-designed application-specific integrated circuits (ASICs) built around machine-learning matrix operations. NVIDIA GPUs are general massively parallel processors with a much larger range of cards, software libraries, clouds, and on-premises systems. That difference affects performance, engineering effort, capacity planning, and the cost of changing direction later.
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TPU and GPU: what you are actually choosing
A TPU is an accelerator designed specifically for machine-learning workloads. Google’s documentation describes TPUs as “custom-developed, application-specific integrated circuits (ASICs) designed to accelerate machine learning (ML) and artificial intelligence (AI) workloads.” Their TensorCores, SparseCores, high-bandwidth memory (HBM), pod interconnects, and XLA compiler are optimized for large, regular tensor programs.
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An NVIDIA GPU combines many programmable CUDA cores with tensor acceleration, HBM or other high-speed memory, and a mature stack of CUDA libraries. GPUs are used for AI training and inference as well as high-performance computing, graphics rendering, and video transcoding. This generality makes them useful when a model contains unusual operators, dynamic control flow, third-party extensions, or code that must also run outside Google Cloud.
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The practical choice is therefore not “ASIC versus GPU” in isolation. It is a complete platform decision involving the accelerator, host machine, compiler, framework integration, networking, storage, quota, pricing model, and the engineering work required to make the workload reliable.
Current hardware specifications
The following figures are vendor specifications, not an application benchmark. TFLOPS describe theoretical low-precision arithmetic and cannot predict the throughput of every model.
| Platform | Per-chip compute listed by Google | Memory per chip | Memory bandwidth | Maximum pod size listed |
|---|---|---|---|---|
| TPU7x (Ironwood) | 2,307 BF16 TFLOPS; 4,614 FP8 TFLOPS | 192 GiB HBM | 7,380 GB/s | 9,216 chips |
| TPU v6e (Trillium) | 918 BF16/FP8 TFLOPS | 32 GiB | 1,638 GB/s | 256 chips |
Google’s current product information says Trillium delivers 67% greater energy efficiency and 4.7 times the peak compute per chip of TPU v5e. The same material lists Ironwood at 42.5 exaFLOPS per pod and four times the per-chip performance of Trillium. These are generation-to-generation product claims; they do not establish that Ironwood will be four times faster on your model.
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Which is better for AI training?
When a TPU is a strong fit
Google recommends TPU7x and TPU v5p for large-scale pre-training because these jobs benefit from high low-precision compute and high interconnect bandwidth. A dense transformer with regular tensor shapes, static or mostly static execution, and a framework path supported by XLA can use the TPU’s matrix units and pod network efficiently. Large synchronous jobs are particularly sensitive to the slowest worker and to all-reduce traffic, so tightly coupled pod networking can be valuable.
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TPUs are less attractive when the training loop branches frequently, performs many small element-wise operations, invokes custom operators that fall back to CPUs, or requires FP64 arithmetic. Irregular workloads can leave matrix units idle or spend disproportionate time compiling and moving data.
When an NVIDIA GPU is the safer training default
Use a GPU when the project depends on CUDA libraries, custom CUDA kernels, specialized communication primitives, or an open-source implementation that is released and tested first for NVIDIA hardware. GPUs are also the lower-risk option for rapidly changing research code: a missing TPU operator can require a rewrite, while a GPU build may run immediately.
GPU capacity is available in many machine families and across more cloud and on-premises environments. That breadth helps when you need to switch from a small development card to a multi-GPU server, reproduce a result in another cloud, or deploy the same model at an edge or private data center. It does not guarantee that a high-end model such as an H100 will be available in your preferred region.
What the published Gemma comparison demonstrates
A May 2026 paper, Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines, compared TPU v5p-8 and TPU v6e-8 with a two-H100 baseline under identical hyperparameters. The authors report that TPU training completed 1.61 times faster at 2.12 times lower training cost. Inference throughput was within 3% of the GPU baseline; TPU time-to-first-token was 235 ms versus 475 ms for the GPUs. Combining training and serving costs was 1.82 times cheaper for that setup.
Those numbers are evidence for that model, software stack, hardware configuration, and workload shape—not a universal TPU multiplier. The same authors describe substantial porting work: configuring the mesh, changing LoRA naming, adding sharding annotations, enabling gradient checkpointing, restructuring the data pipeline, and converting checkpoints. The engineering cost and schedule should be included in any comparison.
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Which is better for inference?
Inference has several different objectives. Interactive applications care about time-to-first-token and tail latency; batch generation cares about tokens per second and utilization; a public API also cares about memory headroom, autoscaling, and the cost of idle capacity.
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Google’s inference guidance maps common choices as follows:
| Workload or constraint | Hardware recommendation in Google’s guide |
|---|---|
| Small models where hourly cost is the priority | NVIDIA L4 |
| Transformer or text-to-image inference focused on cost efficiency | TPU v5e |
| High value on transformer and text-to-image models | TPU v6e |
| Large model that fits on one host | A100 or H100 |
| Most demanding multi-host deployments | H200, B200, or GB200 |
For a 260 GB model, Google’s guide excludes L4 because the largest G2 configuration has only 192 GB of accelerator memory. Some Arm-based systems may also require workload refactoring. A model that barely fits can be a poor production choice: weights, runtime buffers, KV cache, and communication workspace all need memory, and longer sequences increase KV-cache demand.
Google’s May 2025 AI Hypercomputer update describes vLLM on TPU and JetStream. Kirat Pandya, CEO of Osmos, said the company had both systems in scaled production on Trillium and achieved “over 3500 tokens/sec per v6e node for long sequence inference for 70B class models.” That is a vendor-blog quotation attributed to Pandya and Osmos, not an independent result and not a guarantee for every 70B serving configuration.
In practice, benchmark the complete serving path. A kernel-level throughput figure can look excellent while tokenizer time, request queuing, prefill, decoding, network transfer, or autoscaler delays dominate the user-visible latency.
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How to compare cost correctly
Do not compare an accelerator’s hourly sticker price with another accelerator’s hourly sticker price and call the result a business case. Calculate the cost of a useful result:
- Training: cost per completed run, successful epoch, or fine-tuning iteration.
- Inference: cost per million input and output tokens at the required latency and quality.
- Operations: host CPU and RAM, attached storage, networking, logging, checkpoint transfer, retries, and idle time.
- People: migration, debugging, kernel work, compiler tuning, and on-call burden.
- Capacity terms: on-demand rates, commitments, reservations, Spot interruptions, and minimum purchase periods.
Google’s GPU pricing page gives this NVIDIA T4 example: $0.35 per GPU-hour on demand, $0.22 per hour with a one-year commitment, and $0.16 per hour with a three-year commitment. These figures are an example for a T4, not an H100 or B200 price and not the price of a complete machine. GPU rates vary by machine family and region.
TPU prices vary by TPU version, location, and consumption model. TPU and GPU totals should include the same host, storage, network, and software assumptions. If a TPU completes a job in half the time but requires a week of engineering work, the first run may cost more even if later runs are cheaper.
Google Cloud purchasing modes
Google documents four TPU capacity approaches: on-demand, Spot, Flex-start, and future reservations. Spot capacity can be discounted by up to 70%, with a 30-second interruption notice. It is suitable only when the training system checkpoints frequently and can resume safely. A future reservation can guarantee capacity and receive discounted committed-use pricing, but it requires planning and approval. GPU instances also offer on-demand and commitment choices; exact terms and availability depend on region and machine family.
Availability: can you obtain capacity now?
Availability is not one worldwide stock number. It depends on region, project quota, reservation approval, machine type, and whether the request can be interrupted. A product can be generally available while a particular pod size is unavailable in your chosen zone.
Google lists Ironwood (TPU seventh generation) and Trillium (sixth generation) as generally available. Its TPU page lists TPU 8t, optimized for training, and TPU 8i, optimized for inference and reinforcement learning, as “Coming soon.” Treat that roadmap label as a direction, not as capacity you can book today.
NVIDIA capacity spans more Google Cloud models and machine families, so a substitute may be easier to find. High-end GPU capacity can still be constrained, especially for large multi-host reservations. Before committing to a design, check quota, zone availability, reservation lead time, interruption policy, and the fallback hardware you will use if the preferred accelerator is full.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Framework compatibility and migration effort
PyTorch and CUDA code
CUDA code is written for NVIDIA GPUs and their libraries. It will not run unchanged on a TPU. A PyTorch model can sometimes be moved through TPU-supported PyTorch tooling, but “it imports” is not the same as “it has production performance.” Unsupported operators, custom extensions, dynamic shapes, and assumptions about CUDA streams or memory behavior can require code changes.
JAX and XLA-oriented programs are usually the most direct TPU path. XLA compiles a graph for the accelerator, so shape stability, compilation time, and sharding annotations matter. A model with frequent shape changes may repeatedly compile or fail to use the intended layout.
Migration checklist
- Inventory dependencies. List CUDA extensions, fused kernels, custom autograd functions, unsupported operators, data-loader assumptions, and checkpoint formats.
- Run a small correctness test. Compare loss, gradients, logits, and generated text with the same seed and precision. Resolve numerical differences before measuring speed.
- Choose the mesh and sharding. Define data, tensor, and pipeline axes explicitly. Verify that weights, activations, optimizer state, and KV cache are placed where intended.
- Stabilize shapes. Bucket sequence lengths and batch sizes where possible. Measure compile time separately from steady-state execution.
- Move the input pipeline. Ensure preprocessing, storage reads, host-to-device transfer, and prefetching keep the accelerator fed.
- Convert checkpoints. Test a round trip: save on the source platform, load on the target, resume training, and compare evaluation metrics.
- Profile the full step. Measure compute, collective communication, input stalls, recompilation, checkpointing, and host fallback rather than only matrix utilization.
A repeatable benchmark that produces a useful answer
Run the same experiment on the exact TPU and GPU configurations you could purchase. Record the region, machine type, accelerator count, host specification, software versions, serving engine, and pricing model.
- Fix the workload. Use the production model revision, tokenizer, dataset sample, precision, optimizer, sequence length distribution, batch size, and parallelism strategy.
- Warm up both systems. Exclude one-time compilation and image-download costs from steady-state throughput, but record them separately because they affect autoscaling and short jobs.
- Measure training. Report examples per second, time to a target validation metric, peak memory, checkpoint time, restart recovery, and total accelerator plus host cost.
- Measure inference. Test prompt lengths and output lengths that represent real traffic. Report time-to-first-token, inter-token latency, tokens per second, p50 and p95 latency, throughput at a fixed concurrency, and quality.
- Stress failure modes. Interrupt a Spot worker, restart a host, fill the KV cache, and test an oversized request. A platform that is fast only in a clean laboratory run may be expensive in production.
- Compute unit economics. Divide the complete bill by completed training runs or delivered tokens. Include engineering amortization and the utilization level you can realistically sustain.
Decision rules by project type
Choose a TPU when all of these are true
- The model maps cleanly to XLA, JAX, or supported TPU PyTorch tooling.
- The workload is dense and large enough to benefit from high-throughput matrix operations and pod networking.
- Your team can support sharding, compilation, checkpoint conversion, and TPU-specific debugging.
- A benchmark with production shapes shows lower cost per completed result or materially better latency.
- The required region and pod size are obtainable through quota, reservation, or an acceptable interruptible plan.
Choose an NVIDIA GPU when these constraints dominate
- The code relies on CUDA libraries, custom kernels, or rapidly changing open-source implementations.
- You need broad PyTorch, inference-server, and model-format compatibility immediately.
- You expect to move between clouds, on-premises GPU servers, or edge deployments.
- The workload has frequent branching, many element-wise operations, CPU fallback, or FP64 requirements.
- Fast experiments and low migration risk are worth more than a possible TPU efficiency gain.
Use a two-platform strategy only with a clear reason
Keeping GPUs for development and TPUs for a stable training or serving path can work, but it creates two validation targets, checkpoint paths, monitoring systems, and incident playbooks. Adopt that split only when the measured savings or capacity advantage pays for the duplicated operational surface.
Common mistakes and how to avoid them
- Comparing peak TFLOPS. Use completed work per dollar at the model’s actual precision and shape.
- Ignoring compile and porting time. Track one-time engineering and compilation separately from steady-state throughput.
- Benchmarking a toy batch. Include production sequence lengths, concurrency, memory pressure, and data loading.
- Buying for average traffic. Size for peak latency and failure recovery, then calculate the cost of idle capacity.
- Assuming a reservation solves every risk. Confirm quota, zone, pod shape, lead time, commitment term, and fallback capacity in writing.
- Treating Spot as reliable capacity. Use frequent checkpoints and resumable jobs, and include the 30-second interruption notice in the recovery design.
- Forgetting portability. A TPU-optimized implementation may reduce future hardware choices; a GPU implementation may preserve more alternatives but cost more per result.
Bottom line
For supported, dense, large-scale workloads, a TPU can deliver exceptional throughput, energy efficiency, and cost per result. The Gemma 4 31B comparison shows how substantial that advantage can be in one carefully matched setup, while also showing that migration is real engineering work. NVIDIA GPUs remain the practical default for CUDA-dependent software, unusual operators, broad model support, multi-cloud portability, and rapid iteration. Benchmark both platforms with your production workload and price the entire system; the faster accelerator is not necessarily the cheaper service, and the cheaper run is not necessarily the lower-cost project.
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