Best AI inference chip
4 models · updated 2026-07-15
The verdict
Groq LPU leads — 1 of 4 models rank Groq LPU the top pick.
Not unanimous: Claude picks Google TPU; Gemini picks Google TPU; Grok picks Cerebras WSE-3.
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Combined ranking
- 1GPT #1Claude #3Gemini #2Grok #2
Exceptional low-latency, deterministic LLM inference with hundreds of tokens per second, inexpensive GroqCloud access, and an easy OpenAI-compatible API; best overall for practitioners prioritizing responsive text, speech, or agent workloads.
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GPT Exceptional low-latency, deterministic LLM inference with hundreds of tokens per second, inexpensive GroqCloud access, and an easy OpenAI-compatible API; best overall for practitioners prioritizing responsive text, speech, or agent workloads.
Gemini Its software-defined SRAM architecture eliminates memory-wall latency bottlenecks, offering deterministic, ultra-low-latency autoregressive token generation that is unmatched for real-time agentic workflows.
Grok Deterministic low-latency tensor streaming architecture delivers industry-leading tokens/sec per user and consistent real-time performance (hundreds of t/s on 70B models, often 10-18x GPU throughput) with excellent efficiency; strongest for interactive, latency-sensitive serving.
Claude Deterministic compiler-scheduled architecture gives class-leading low latency, a generous free tier, and the largest practitioner adoption of any GPU challenger via GroqCloud — the easiest first taste of non-GPU inference
Where it falls shortper GPT Supports a curated model catalog rather than arbitrary models and lacks the GPU ecosystem’s flexibility.
per Claude Low per-chip memory means big deployments need huge racks, so it only makes sense as a hosted API and model coverage lags GPU-land
per Gemini Highly constrained by physical on-chip SRAM capacity, requiring massive clusters or disaggregated GPU/CPU architectures to handle prefill phases and large models.
- 2GPT #2Claude #2Gemini #5Grok #1
Extreme on-chip memory and compute density enable unmatched token throughput (e.g., 1,800+ t/s on Gemma 4, 35-57x faster than GPUs on large models) for massive models without multi-chip sharding overhead; ideal for high-throughput inference on frontier-scale LLMs where raw speed and efficiency per wafer matter most (assumes access via cloud or cluster).
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Grok Extreme on-chip memory and compute density enable unmatched token throughput (e.g., 1,800+ t/s on Gemma 4, 35-57x faster than GPUs on large models) for massive models without multi-chip sharding overhead; ideal for high-throughput inference on frontier-scale LLMs where raw speed and efficiency per wafer matter most (assumes access via cloud or cluster).
GPT Near-tied with Groq for first: wafer-scale memory bandwidth enables outstanding generation speed on large and reasoning models, while Cerebras Inference offers competitive per-token pricing and dedicated custom-weight deployments.
Claude Wafer-scale SRAM delivers the fastest measured tokens/sec on large open models (Llama, Qwen) by a wide margin, available as a simple OpenAI-compatible API — the clearest raw-speed proof that GPUs can be beaten; near-tie with Groq, ranked above on large-model throughput headroom
Gemini Wafer-scale integration bypassing traditional chip-to-chip bottlenecks to deliver industry-leading single-system throughput and up to 21 PB/s memory bandwidth.
Where it falls shortper GPT Broad custom-model deployment and predictable capacity generally require an enterprise arrangement.
per Claude Capacity is scarce and pricing/context limits make it an API service, not a platform you deploy or fine-tune on
per Gemini Extremely high acquisition cost at million-dollar scale and specialized power and cooling data center requirements, putting it out of reach for all but the largest enterprises.
- 3GPT #3Claude #1Gemini #1Grok —
The only non-GPU silicon running frontier-scale production inference today — mature JAX/XLA and growing vLLM support, strong perf-per-dollar on GCP, and Ironwood is explicitly inference-optimized; assumes the practitioner is willing to run in Google Cloud rather than own hardware
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Claude The only non-GPU silicon running frontier-scale production inference today — mature JAX/XLA and growing vLLM support, strong perf-per-dollar on GCP, and Ironwood is explicitly inference-optimized; assumes the practitioner is willing to run in Google Cloud rather than own hardware
Gemini Delivers the best balance of cost-efficiency, software maturity via PyTorch/XLA and JAX, and cloud accessibility for mainstream LLM deployment, offering a ~4.7x price-performance improvement over previous generations.
GPT The strongest hyperscale option, combining enormous pod-scale compute, high-bandwidth memory, strong dense and MoE performance, and mature JAX/XLA infrastructure for demanding inference fleets.
Where it falls shortper GPT Expensive, region- and quota-constrained Google Cloud capacity makes it poor value for ordinary or small deployments.
per Claude GCP-only lock-in — you can't buy one, and porting CUDA-centric stacks still costs real engineering time
per Gemini Locked exclusively to Google Cloud Platform, preventing on-premises deployments or multi-cloud flexibility.
- 4GPT #4Claude #4Gemini #3Grok —
Extremely cost-effective for AWS-native users, offering up to 50% better performance-per-watt than standard cloud instances, and scales efficiently via NeuronLink interconnects.
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Gemini Extremely cost-effective for AWS-native users, offering up to 50% better performance-per-watt than standard cloud instances, and scales efficiently via NeuronLink interconnects.
GPT Inf2 instances offer strong sustained throughput per dollar, scalable NeuronLink configurations, 32 GB HBM per chip, and practical integration with an organization already operating on AWS.
Claude The pragmatic cost play — 30-50% cheaper inference than comparable GPU instances inside the cloud most teams already use, with vLLM/HuggingFace Optimum integration maturing steadily
Where it falls shortper GPT Neuron compilation, operator gaps, and AWS lock-in add materially more porting friction than deploying on GPUs.
per Claude Neuron SDK friction is real — newest model architectures and custom kernels land late or not at all, so it's not for teams chasing the frontier
per Gemini Dependent on the AWS Neuron SDK, which requires ahead-of-time model compilation and lacks native support for many custom or experimental neural network operators.
- 5GPT —Claude —Gemini —Grok #3
Reconfigurable dataflow with tri-tier memory excels at large MoE and agentic models (e.g., high t/s on 671B+ with far fewer chips than GPUs, 3-5x speed/efficiency gains); strong value in efficiency and specialized throughput for complex inference.
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Grok Reconfigurable dataflow with tri-tier memory excels at large MoE and agentic models (e.g., high t/s on 671B+ with far fewer chips than GPUs, 3-5x speed/efficiency gains); strong value in efficiency and specialized throughput for complex inference.
- 6GPT #5Claude #5Gemini —Grok —
Its dataflow architecture, large distributed memory, and high-throughput inference make it compelling for enterprise-scale LLM serving, especially where SambaNova’s full-stack system fits operational requirements.
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GPT Its dataflow architecture, large distributed memory, and high-throughput inference make it compelling for enterprise-scale LLM serving, especially where SambaNova’s full-stack system fits operational requirements.
Claude Reconfigurable dataflow chip serves very large models (405B-class) at high speed with fast model-switching, offered both as API and on-prem racks — one of the few challengers an enterprise can actually install
Where it falls shortper GPT Limited self-service availability and a smaller software ecosystem make it unsuitable for most independent developers.
per Claude Small ecosystem and enterprise-sales motion put it out of reach of individual practitioners; API capacity and model list are thin versus Groq/Cerebras
- 7GPT —Claude —Gemini #4Grok —
Offers a developer-first, cost-disruptive alternative to HBM-based systems by using GDDR6 memory and on-die RISC-V cores to eliminate host CPU overhead, all running on an open-source compiler stack; in a near-tie with AWS Inferentia2 for general cost-efficiency but favored for open-source code control.
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Gemini Offers a developer-first, cost-disruptive alternative to HBM-based systems by using GDDR6 memory and on-die RISC-V cores to eliminate host CPU overhead, all running on an open-source compiler stack; in a near-tie with AWS Inferentia2 for general cost-efficiency but favored for open-source code control.
Where it falls shortper Gemini The GDDR6 memory interface provides significantly lower raw bandwidth than HBM3/e, limiting peak throughput for memory-bound workloads that cannot be tiled into SRAM.
Just missed the top 5
GPT Qualcomm Cloud AI 100 Ultra — efficient and strong for supported inference workloads, but limited cloud availability and ecosystem breadth · Tenstorrent Wormhole — accessible, open developer-oriented hardware, but its software maturity and large-model performance trail the top production platforms
Claude Tenstorrent Blackhole — the only challenger you can buy as a card with an open-source stack, but software maturity still trails too far for typical production use · Intel Gaudi 3 — solid price/performance on paper but weak adoption and a roadmap folded toward Falcon Shores makes betting on it risky
Gemini Etched Sohu — it is hard-wired specifically for Transformer models, offering zero architectural flexibility for non-transformer models like diffusion or CNNs · Microsoft Maia 200 — it is limited to Microsoft's internal infrastructure and Azure-locked services, making it unavailable for general cloud or on-premises deployment
Grok Tenstorrent Galaxy Blackhole — open RISC-V approach offers good scale-out and cost for certain inference but trails leaders in proven large-model throughput benchmarks
By model
ChatGPT
- 1.Groq LPU
- 2.Cerebras WSE-3
- 3.Google TPU
- 4.AWS Inferentia2
- 5.SambaNova SN40L
Claude
- 1.Google TPU
- 2.Cerebras WSE-3
- 3.Groq LPU
- 4.AWS Inferentia2
- 5.SambaNova SN40L
Gemini
- 1.Google TPU
- 2.Groq LPU
- 3.AWS Inferentia2
- 4.Tenstorrent Blackhole
- 5.Cerebras WSE-3
Grok
- 1.Cerebras WSE-3
- 2.Groq LPU
- 3.SambaNova SN50
This ranking moves
We re-poll all four models weekly. Get one short email when a #1 flips.
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled weekly