> ## Documentation Index
> Fetch the complete documentation index at: https://tensorfuse.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# NVIDIA alternatives for LLM inference

> Compare alternate inference silicon: AMD GPUs, AWS Trainium and Inferentia, Google TPUs, specialized accelerators, CPUs, and Apple silicon.

A model fits in memory, but users still wait too long or the serving bill is too high. Choosing alternate inference silicon starts with identifying that bottleneck. Compare the complete system: model support, memory, compute, interconnect, serving software, and deployment location. Peak chip specifications alone cannot establish the best option for your workload.

## Start with the part that is slow

Prefill processes the input, while decode generates the response one token at a time. Long prompts can create substantial compute work. At low batch sizes, decode often spends much of its time moving model weights and attention state through memory. At higher concurrency, batching can improve weight reuse while increasing queueing and cache requirements.

These are tendencies to investigate, not fixed labels for every model. Mixture-of-experts routing, attention architecture, quantization, and [speculative decoding](/docs/guides/inference/speculative-decoding) change the balance. Measure prefill and decode separately using the [benchmarking guide](/docs/guides/inference/benchmarking).

## Which alternatives should you evaluate?

| Option                                                                               | Software or access path                       | Main evaluation question                                                              |
| ------------------------------------------------------------------------------------ | --------------------------------------------- | ------------------------------------------------------------------------------------- |
| [AMD Instinct GPUs](/docs/guides/inference/hardware/amd-rocm)                             | ROCm-compatible serving engines               | Does the exact model/kernel stack meet the target on the available GPU topology?      |
| [AWS Inferentia and Trainium](/docs/guides/inference/hardware/aws-neuron)                 | AWS Neuron and its supported integrations     | Can the model and request shapes use the compiled serving path efficiently?           |
| [Google TPUs](/docs/guides/inference/hardware/google-tpu)                                 | Cloud TPU and TPU-compatible serving software | Does the model map well to the chosen slice and software backend?                     |
| [Groq, Cerebras, and SambaNova](/docs/guides/inference/hardware/specialized-accelerators) | Provider APIs or vendor-specific systems      | Are the required model, features, deployment terms, and latency available together?   |
| [CPUs and Apple silicon](/docs/guides/inference/hardware/cpu-apple-silicon)               | CPU backends, llama.cpp, or MLX               | Can local or existing hardware satisfy the real concurrency and response-time target? |

The linked explainers cite upstream architecture and software documentation, reviewed September 6, 2026. They describe evaluation paths, not new Tensorfuse hardware integrations. Use the [deployment configuration reference](/docs/concepts/configuration) for documented Tensorfuse resource options.

## Separate memory capacity from memory bandwidth

Capacity determines whether weights, runtime buffers, and active request state fit. Bandwidth determines how quickly those bytes can move. Adding memory can prevent out-of-memory failures without improving generation speed. Splitting a model across more devices adds memory and compute, but also communication.

For example, if one device cannot hold the model at the required context length, first compare quantization and sharding options. If the model fits but decode is slow, profile memory traffic and kernel execution. If individual requests are fast but users queue, test batching and replica capacity before replacing the hardware.

## Compare deployment economics at the same target

Keep model quality, input/output lengths, and latency targets fixed. Include host resources, storage, networking, idle capacity, compilation, engineering effort, and any provider minimum commitment. Compare cost per successful task within the latency target, alongside throughput and failures.

Also choose where execution belongs. Calling a hosted API, operating a cloud accelerator, and running a model on a laptop create different data paths and operational responsibilities. An OpenAI-compatible API does not make those deployment models equivalent.

## Related guides

<CardGroup cols={2}>
  <Card title="Benchmark inference" href="/docs/guides/inference/benchmarking">Measure the bottleneck and compare cost at a fixed service target.</Card>
  <Card title="Choose the model" href="/docs/guides/inference/open-models">Establish quality and compatibility before selecting hardware.</Card>
</CardGroup>

## Put this into practice

Start with the [practical inference how-to guides](/docs/guides/inference/how-to/reduce-cost#4-try-alternate-silicon-with-the-right-software) to install a compatible runtime, bring up an endpoint, and test each hardware path.