Fastest LLM Inference Platform Comparison: Groq vs Cerebras vs SambaNova vs GMI Cloud

Groq's LPU delivers 476 tokens per second on gpt-oss-120B. Cerebras reports 3,000 tokens per second on the same model. Both numbers are real, independently verified, and roughly 10 to 20 times faster than NVIDIA GPU inference on equivalent hardware. For most workloads, these numbers also do not matter.The decision between specialized inference hardware and GPU cloud inference is not primarily about tokens per second. It is about whether the models your production system depends on are available on the platform you are evaluating.This piece puts the speed gap in context, maps the numbers to the workloads where they are decision-relevant, and compares Groq and Cerebras against GPU inference running DeepSeek-V4-Pro, Gemini 3.5 Flash, and GPT-5.4-mini.

How Specialized Chips Achieve 5-20x Speed Over GPU Inference

Both Groq's LPU (Language Processing Unit) and Cerebras's WSE-3 (Wafer Scale Engine) solve the same bottleneck: the memory bandwidth wall that limits GPU inference.

During LLM token generation, the GPU must load the full model's weights from DRAM into compute cores for every forward pass. For a 70B parameter model, that means moving roughly 140GB of data on every token. GPU HBM bandwidth, at 3.35-4.8 TB/s on H100 and H200, limits how fast this can happen.

Groq's LPU uses on-chip SRAM that keeps model weights resident and eliminates the memory transfer step entirely. Cerebras's WSE-3 takes this further: a single wafer-scale chip with 900,000 cores and enough on-chip memory to store large models without external DRAM transfers. The result is TTFT under 100ms on Groq and 80-150ms on Cerebras, compared to 400-600ms typical on GPU inference.

GPU providers have closed part of this gap through speculative decoding, paged attention, KV-cache optimization, and TensorRT-LLM. On GMI Cloud, these optimizations narrow the speed difference enough that the practical gap for many workloads is significantly smaller than the raw benchmark numbers suggest. But for single-request latency at low concurrency, the architectural advantage of specialized silicon is real.

The Speed Numbers and What They Cost

Provider	Model	TPS	TTFT	Price (input/output per 1M)
Groq	Llama 3.3 70B	~394	sub-100ms	$0.59 / $0.79
Groq	gpt-oss-120B	~476	sub-100ms	$0.15 / $0.60
Cerebras	gpt-oss-120B	~3,000	80-150ms	$0.35 / $0.75
Cerebras	Llama 4 Scout	~2,600	80-150ms	varies
GMI Cloud	Gemini 3.5 Flash	~278	15s (high), <5s (low)	$1.50 / $9.00
GMI Cloud	DeepSeek-V4-Pro	~55-60	varies	$1.39 / varies
GMI Cloud	GPT-5.4-mini	~100-150	varies	$0.40 / $2.50

The speed gap is largest at low concurrency and smallest at high concurrency. GPU infrastructure processes batched requests efficiently. As concurrency increases, the per-request speed advantage of specialized chips diminishes because GPUs can parallelize across the batch.

The use case where the speed gap is genuinely decision-relevant is voice AI.A real-time voice assistant has an end-to-end latency budget of roughly 500 to 800 milliseconds per conversational turn. That budget covers speech-to-text, LLM inference, text-to-speech, and network transmission. On standard GPU inference, the LLM step alone consumes 400 to 600 milliseconds for a moderate response, leaving almost nothing for STT and TTS. On Groq's LPU with sub-100ms TTFT, the LLM step becomes a minor contributor to the total latency rather than the dominant one.

For batch document processing, content generation pipelines, or any workload where responses queue rather than stream to a waiting human, the difference between 60 TPS and 476 TPS on the same model is a cost and throughput question, not a user experience question.

The Model Catalog Constraint Is Not a Small Caveat

Groq and Cerebras serve only models that have been explicitly ported and optimized for their custom silicon. The result is catalogs of 4 to 15 models each, all open-source.

Groq's current catalog covers Llama variants, DeepSeek R1 distilled variants, GPT-OSS, Kimi K2, and Qwen models. No GPT-5, no Claude, no Gemini. Adding a new model to Groq depends on Groq's engineering roadmap, not on model availability.

Cerebras has a smaller catalog, with models selected for WSE-3 compatibility and memory fit.

For enterprise deployments where the pipeline includes GPT-5.4-mini for OpenAI API compatibility, Gemini 3.5 Flash for Google ecosystem integration, or DeepSeek-V4-Pro through the official DeepSeek API, specialized chip platforms simply do not offer these options. A hybrid architecture, with Groq for latency-sensitive open-source model calls and a GPU cloud for proprietary model calls, is operationally possible but adds routing complexity, multiple API relationships, and inconsistent observability.

The practical implication is that the fastest inference platform for a specific workload is whichever platform runs the model that workload requires.

Three Models on GPU Inference That Cover the Speed-Cost Spectrum

GMI Cloud provides access to DeepSeek-V4-Pro, Gemini 3.5 Flash, and GPT-5.4-mini through a single API. These three models illustrate the GPU inference speed and cost range available without the model catalog restrictions of specialized chip providers.

Gemini 3.5 Flashdelivers 278 tokens per second at its standard thinking level on the first-party API, ranking second among all measured frontier models by TPS. At $1.50 per million input tokens, it combines near-top-tier speed with a 1M token context window. For interactive features where generation speed matters and the application uses Google-compatible tooling, this is the fastest GPU-served frontier model currently available.

GPT-5.4-mini, at $0.40 per million input tokens and $2.50 per million output tokens, covers the mid-tier workload range. It is available across the OpenAI API, Codex, and ChatGPT, which matters for teams building on OpenAI's tool ecosystem. Generation speed is moderate compared to Gemini 3.5 Flash but significantly faster than reasoning-heavy models.

DeepSeek-V4-Progenerates at approximately 55 to 60 tokens per second on the first-party API, with pricing at $1.39 per million input tokens. The MIT-licensed open-weight model trails frontier closed models by 3 to 6 months on benchmark scores while costing a fraction of comparable performance from proprietary providers. For complex reasoning and coding tasks where output quality drives downstream value more than generation speed, V4-Pro's capability-to-cost ratio is difficult to match on specialized chip platforms that do not carry the model.

All three are accessible on GMI Cloud under a single API key with per-request billing. Model documentation and console access are atconsole.gmicloud.aiand pricing atgmicloud.ai/en/pricing.

Choose the Platform for the Workload That Determines Your Architecture

The framing of "fastest LLM inference platform" implies a single answer. The practical answer is specific to the workload.

For voice AI pipelines where end-to-end latency under 500ms per turn is a hard requirement and an open-source model at the 70B scale fits the quality bar, Groq or Cerebras is the right choice, and no GPU optimization currently matches their TTFT profile for single-request low-concurrency inference.

For any production system that depends on GPT-5.4-mini, Gemini 3.5 Flash, or DeepSeek-V4-Pro through their official APIs, those models are not available on Groq or Cerebras, and GPU cloud inference is the only path.

For batch workloads, document processing, and any application where requests are queued rather than served to a waiting user, the speed gap between 60 TPS and 476 TPS on the same model does not translate into a user experience difference. It translates into throughput capacity and cost per token, where the tradeoff between specialized chip pricing and GPU cloud flexibility becomes the actual decision.

The architecture that runs fastest for a given system is the one that removes the actual bottleneck in that system's latency profile, not the one with the highest TPS benchmark on a model you may not be using.