Fastest LLM Inference in 2026
The fastest LLM inference in 2026 doesn't run on GPUs. Specialty inference silicon (Cerebras WSE-3, Groq LPU, SambaNova RDU) delivers 400-2,200 tokens per second on Llama 3.3 70B — 5-20x faster than the best H100-based providers. The architectural advantage is on-chip memory: when an entire 70B model fits in SRAM or HBM3 next to the compute units, the memory-bandwidth bottleneck that limits GPU inference disappears. For interactive applications, voice agents, and reasoning workloads where time-to-first-token compounds, the speed-tier providers are the only credible choice.
The best llm api providers tools in 2026 are Cerebras Inference API ($0.1–$6/per million tokens), Groq ($0–$0/per million tokens), and SambaNova Cloud ($0.1–$5/per million tokens). The fastest LLM inference in 2026 is Cerebras at 2,000+ tokens per second on Llama 3.3 70B — 18x faster than GPT-4o. Groq runs at 600-840 tok/s with the best free tier in the category. SambaNova hits 400-580 tok/s with particular strength on reasoning models like DeepSeek R1. For frontier proprietary models, OpenAI GPT-5 mini at 150 tok/s is the fastest available.
The fastest LLM inference in 2026 is Cerebras at 2,000+ tokens per second on Llama 3.3 70B — 18x faster than GPT-4o. Groq runs at 600-840 tok/s with the best free tier in the category. SambaNova hits 400-580 tok/s with particular strength on reasoning models like DeepSeek R1. For frontier proprietary models, OpenAI GPT-5 mini at 150 tok/s is the fastest available.
Our Rankings
Cerebras Inference API
Cerebras Inference runs on the WSE-3 wafer-scale chip and delivers 2,000+ tokens per second on Llama 3.3 70B — roughly 18x faster than GPT-4o and 3x faster than Groq. The architectural advantage is that the entire 70B model sits on a single chip's on-chip SRAM, eliminating the memory-bandwidth bottleneck that limits GPU-based inference. For interactive applications, voice agents, and reasoning workloads where time-to-first-token compounds, Cerebras is the only provider that delivers sub-second responses on full-size models.
- 2,000+ tokens/sec on Llama 3.3 70B (18x faster than GPT-4o)
- Sub-100ms time-to-first-token on most prompts
- Free developer tier with meaningful rate limits
- OpenAI-compatible API — drop-in replacement
- Smaller model catalog (Llama, Qwen, DeepSeek main)
- Limited fine-tuning support vs GPU providers
- Per-token cost ($0.85/M) higher than batch-optimized providers
Groq
Groq runs on its custom LPU (Language Processing Unit) inference architecture and delivers 600-840 tokens per second on Llama 3.3 70B at competitive prices. Groq's edge over GPU providers is deterministic latency — no thermal throttling, no batching variance — making it the right choice for production workloads where p99 latency matters more than peak throughput. The free tier is genuinely usable: 30 req/min on most models with no credit card required.
- 600-840 tokens/sec on Llama 3.3 70B
- Free tier with 30 req/min — best in class
- Deterministic latency (no GPU thermal variance)
- OpenAI-compatible API
- Limited model catalog vs Together AI
- No fine-tuning support
- LPU architecture makes scaling to larger models gradual
SambaNova Cloud
SambaNova Cloud runs on the Reconfigurable Dataflow Unit (RDU) and delivers 400-580 tokens per second on Llama 3.3 70B with particular strength on reasoning models (DeepSeek R1, Qwen QwQ). The architectural advantage is on-chip HBM3 plus dataflow execution, producing consistently high throughput on long-context and chain-of-thought workloads. For agents and reasoning-heavy applications, SambaNova is often faster end-to-end than higher-tokens-per-second providers because it handles long contexts more efficiently.
- 400-580 tokens/sec on Llama 3.3 70B
- Strong on reasoning models (DeepSeek R1, Qwen QwQ)
- Free tier available
- Long-context efficiency (better than competitors at 32K+ tokens)
- Smaller model catalog than Together AI or Fireworks
- Less production-tested for general chatbot workloads
- Documentation thinner than Cerebras or Groq
Fireworks AI
Fireworks AI runs custom CUDA kernels (FireAttention, FireLens, speculative decoding) on H100/H200 GPUs, delivering 200-300 tokens per second on Llama 3.3 70B — the fastest GPU-based inference for production workloads. Unlike specialty silicon (Cerebras, Groq, SambaNova), Fireworks runs on commodity hardware with broader model support including custom fine-tunes, vision-language models, and embeddings. For teams that need both speed and model breadth, Fireworks is the practical pick.
- 200-300 tokens/sec on Llama 3.3 70B (4x faster than vLLM baseline)
- Largest model catalog among speed-tier providers
- Custom fine-tuning supported
- Function calling and structured outputs
- Slower than specialty silicon (Cerebras, Groq)
- Higher cost per token than Together AI
- Cold-start latency on lower-traffic models
Together AI
Together AI runs at 150-250 tokens per second on Llama 3.3 70B with mature batching and dedicated capacity options. The throughput edge over commodity vLLM comes from custom inference optimizations (FlashAttention 3, speculative decoding) and aggressive H100 utilization. For high-volume workloads, Together's dedicated capacity ($1.00-$9.95/hour) delivers predictable throughput at scale better than per-token billing on faster providers.
- 150-250 tokens/sec on Llama 3.3 70B
- Dedicated capacity options for predictable scaling
- Largest open-source model catalog (50+)
- Mature SLAs and US data residency
- Slower than Cerebras, Groq, or SambaNova
- Cold-start latency on niche models
- Per-token cost not the cheapest at this speed
OpenAI API
OpenAI's GPT-5 and GPT-5 mini run at 100-150 tokens per second — slower than specialty silicon but still the fastest you'll get on a frontier proprietary model. For applications where intelligence per token matters more than raw speed (reasoning, agents, complex extraction), GPT-5 mini at 150 tok/s often outperforms a 600 tok/s open-source model on equal wall-clock task time because each token does more work. OpenAI is also the most production-tested API at scale.
- 100-150 tokens/sec on GPT-5 mini
- Frontier model intelligence — outperforms slower providers on hard tasks
- Most production-tested API in the industry
- SOC 2, HIPAA, enterprise SLAs
- Slower per-token speed than specialty silicon
- $1.25-$10 per 1M tokens — 10-50x more expensive than open-source
- Rate limits on lower tiers can constrain throughput
Evaluation Criteria
- throughput
Tokens per second
- latency
Time to first token and p99 latency
- models
Available models at this speed
- cost
Per-token cost at this speed tier
How We Picked These
We evaluated 6 products (last researched 2026-05-07).
Output throughput on Llama 3.3 70B reference workload
Latency before streaming begins
Tail latency under production load
Available models at this speed tier
Per-token cost weighted by speed delivered
Frequently Asked Questions
01 What is the fastest LLM inference provider in 2026?
Cerebras Inference is the fastest at 2,000+ tokens per second on Llama 3.3 70B, running on the WSE-3 wafer-scale chip. Groq runs at 600-840 tok/s on its custom LPU. SambaNova Cloud delivers 400-580 tok/s with strong reasoning model performance. For GPU-based inference, Fireworks AI tops the chart at 200-300 tok/s on H100/H200.
02 Why is Cerebras so much faster than GPT-4 or Claude?
Cerebras runs an entire 70B model in on-chip SRAM on a single wafer-scale chip, eliminating the memory-bandwidth bottleneck that limits GPU inference. Standard GPU inference is bottlenecked by moving model weights from HBM to compute on every token; Cerebras keeps everything on-die. The result is 18x faster output throughput on Llama 3.3 70B vs OpenAI's GPT-4o.
03 Should I use Groq or Cerebras for production?
Cerebras for highest peak throughput (2,000+ tok/s) on Llama and Qwen. Groq for the best balance of speed (600-840 tok/s) and price (lowest in the speed tier) plus the strongest free tier (30 req/min, no card required). Most teams start on Groq's free tier, prove the workload, then evaluate Cerebras for max-speed needs. For frontier-model intelligence, neither replaces OpenAI or Claude.
04 Does fast LLM inference cost more?
Not necessarily. Groq is among the cheapest providers ($0.05-$0.79 per million tokens) AND among the fastest. Cerebras costs more ($0.10-$6/M) but the speed-per-dollar is still excellent. The trade-off is model selection — speed-tier providers focus on a smaller catalog of well-optimized models (Llama, Qwen, DeepSeek) rather than the 50+ models Together AI hosts.
05 Can I get fast inference on GPT-5 or Claude Sonnet?
Not at Cerebras/Groq speeds. Frontier proprietary models (GPT-5, Claude Sonnet, Gemini 2.5 Pro) run on the model owner's infrastructure — OpenAI runs GPT-5 at 100-150 tok/s on GPT-5 mini, Claude Sonnet runs at 80-120 tok/s. Open-source models on specialty silicon are 5-20x faster, but they're different models. Choose based on what intelligence vs speed trade-off your application needs.
06 What's the fastest LLM API for voice agents?
Cerebras or Groq. Voice agents need sub-300ms time-to-first-token to feel responsive, and only specialty silicon delivers that consistently on full-size models. Cerebras typically hits 50-100ms TTFT, Groq 100-200ms TTFT. GPU-based providers (Fireworks, Together) are usable at 300-500ms but feel laggy in voice loops. Cost-wise, Groq is the more practical choice for high-volume voice workloads.
07 How does specialty silicon (LPU, WSE) compare to H100?
On a per-token basis: Cerebras WSE-3 delivers 18x the throughput of H100 on 70B-parameter models due to on-chip SRAM eliminating memory bottlenecks. Groq LPU delivers 5-7x. SambaNova RDU is similar to Groq. The trade-offs: specialty silicon doesn't support training or fine-tuning, so it's purely an inference play. For inference-only workloads at scale, the economics often favor specialty silicon.
08 Is fast LLM inference worth it for chatbots?
Yes for interactive applications, no for batch processing. For a chatbot where users wait for the response, going from 100 tok/s (GPT-4o) to 600 tok/s (Groq) takes a 500-token response from 5 seconds to under 1 second — a UX-meaningful difference. For batch summarization or async data pipelines, latency doesn't matter and price-per-token wins; use Together AI or Fireworks instead.
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