Fastest LLM Inference: What Makes Models Fast & How to Get It
If you build anything where the model's reply needs to feel instant — voice interfaces, coding autocomplete, customer-facing chat, tight agent loops — fastest LLM inference is not a nice-to-have. It is the product. But "fast" is a surprisingly slippery word in this space. Two models can both claim to be "the fastest LLM API" while each dominating on a completely different metric. Getting the fastest result in practice means understanding what actually determines inference speed, which providers compete on which axes, and what you can do architecturally to squeeze every millisecond out of a stack you don't fully control.
This article covers the whole picture: the hardware and software factors that make inference fast, the two key speed metrics you should always track separately, practical techniques to minimize latency in production, and how routing across multiple providers automatically gets you the best of all worlds.
What actually determines inference speed
Speed does not come from one place. It is the product of several layered factors, and changing any one of them — without touching the others — can meaningfully shift your observed latency or throughput.
Model size
Smaller models are faster, everything else equal. A 7B-parameter model generates tokens faster than a 70B one because fewer weights need to be loaded and multiplied per token. The practical consequence: for latency-critical paths, a well-prompted small model often beats a frontier-size model, even if the latter is "smarter." The fastest AI model for your use case is frequently the smallest one that meets your quality bar — not the most capable one available.
Specialised inference hardware
General-purpose GPUs were not designed around the sequential, memory-bound nature of autoregressive token generation. Specialised inference accelerators attempt to close that gap in different ways:
- LPUs (Language Processing Units), such as the hardware behind Groq, use a deterministic, software-scheduled architecture designed specifically for the token-by-token decode loop. The goal is to eliminate the memory-bandwidth unpredictability that throttles conventional accelerators.
- Wafer-scale silicon, as used by Cerebras, integrates the entire inference workload on a single enormous chip rather than a rack of smaller GPUs wired together. By removing inter-chip communication from the hot path, this architecture targets very high sustained throughput.
Neither approach is universally "best" — both are strong on different model sizes, concurrency levels, and workload shapes. Benchmark your actual traffic before committing to either. See Groq vs Cerebras for a detailed hardware comparison.
Quantization
Reducing a model's numerical precision — for example from 32-bit to 8-bit or 4-bit representations — shrinks the memory footprint of the weights and speeds up the arithmetic per token. Most production inference endpoints use some form of quantization. The tradeoff is a possible reduction in output quality; how much depends on the model, the quantization method, and the task. In practice, modern quantization techniques often preserve quality well enough that the speed gain is worth it for most non-safety-critical applications.
Batching
Inference hardware is most efficient when processing multiple requests at once. Providers use continuous or dynamic batching to fill their accelerators. The knock-on effect for you: at high concurrency, throughput often stays high while per-request latency can creep up slightly. At low concurrency, you may get lower latency but the hardware is less utilized. This is one reason your benchmarks need to reflect your actual request rate, not isolated single-request tests.
Output length
The number of tokens the model generates is a direct multiplier on generation time. A 500-token response takes roughly five times as long to stream as a 100-token one, on the same model and hardware. Capping max_tokens and writing prompts that discourage verbose padding are among the highest-leverage code changes you can make — they cost you nothing in infrastructure and can halve wall-clock response times for many workloads.
TTFT vs tokens per second: the metric split you need
Treating inference latency as one number is the most common measurement mistake. There are two fundamentally different metrics, they have different causes, and optimising for one does not automatically help the other.
| Metric | What it measures | What drives it | What to do about it |
|---|---|---|---|
| Time to first token (TTFT) | Milliseconds until the first token arrives | Network round-trip, provider queue depth, prompt prefill cost | Pick providers with low queue time; keep prompts concise; stream immediately |
| Tokens per second (TPS) | Generation speed once streaming starts | Model size, hardware architecture, quantization, batch occupancy | Choose a fast-inference provider; use smaller models where quality permits; cap output length |
For interactive applications, TTFT is usually what users feel as slowness — the silent pause before any text appears. For pipelines that process large volumes of text (summarization, extraction, translation), total throughput in tokens per second often matters more than TTFT. Know which metric you're actually optimizing before reaching for a fix. Reducing LLM latency has a deeper treatment of both.
Providers known for fast inference
The major cloud providers (OpenAI, Anthropic, Google) optimize for capability and reliability. For raw speed on open-weight models, a second tier of specialized inference platforms has emerged:
- Groq — LPU-based inference, consistently benchmarks near the top for TTFT and streaming speed on supported models.
- Cerebras — Wafer-scale inference, well-regarded for very high sustained throughput on focused model variants.
- Together AI, Fireworks AI, DeepInfra — GPU-cluster providers that focus on efficient serving of open-weight models, often competitive on both speed and price.
Headline performance figures from any provider's marketing change quickly. The only reliable approach is to run benchmarks against your own prompts, at your own concurrency, from your own deployment region. What is fastest for a 1k-token summarization task at 10 requests per minute may not be fastest for a 50-token autocomplete at 500 requests per minute.
Practical ways to get the fastest LLM API result in production
Stream every response
Enable streaming (stream: true or SSE mode) on every latency-sensitive call. Perceived latency collapses from total completion time to TTFT, which is often one-fifth or less of the full duration. This is the single highest-leverage change for interactive applications and costs nothing except a slightly different response-handling loop.
Cap output length aggressively
Set max_tokens to the minimum your use case genuinely requires. Prompt engineering that tells the model to be concise reinforces this further. Together these changes are often worth 40–60% of total latency reduction with no infrastructure change at all.
Route latency-critical paths to specialized providers
Not every call needs to go to the same endpoint. You can send your latency-critical real-time flows to a fast-inference provider while batch or quality-critical flows go to a frontier model. A gateway layer — rather than hardcoded provider URLs in application code — makes this routing cheap to change as your needs evolve.
Race multiple providers and take the fastest response
For workloads where latency is the top priority, the most robust technique is to fire the same request to several providers simultaneously and return whichever responds first. This is called LLM racing (sometimes provider racing or parallel inference). It eliminates the impact of queue spikes, cold starts, and regional slowness on any single provider — because you're not committed to any one of them. The statistical effect is significant: the minimum of three independent completion times is consistently lower than any single expected value.
The tradeoff is that you pay for the winning response only if your gateway drops the losing ones immediately. If your implementation lets all three complete and bills you for all three, the cost triples. A proper implementation cancels or ignores the slower responses the moment the first one arrives. This is exactly what flo2's AI Racing feature does — it fans requests out to multiple providers and returns the first to complete, at no token markup.
Measure TTFT and TPS separately, in production conditions
Measure both metrics in your actual deployment environment, not from a laptop on a different continent. Use a consistent sample of real prompts, at realistic concurrency. Track both p50 and p95 (or p99) — providers that look similar at median can diverge sharply at the tail, and it's the tail that users notice. Re-benchmark periodically: provider performance drifts as they add capacity, change batching strategies, or add new model versions.
Putting it together with a gateway
Managing multiple fast-inference providers by hand — different keys, different base URLs, different response shapes, per-provider SDKs — adds significant operational overhead. An OpenAI-compatible gateway normalizes all of this: you configure one key, point your existing code at one base URL, and the routing, racing, fallback, and caching happen transparently.
flo2 was built for exactly this pattern. You bring your own provider keys (so there is no token markup), and a single flo2 key routes to whatever provider is cheapest or fastest for each request. The AI Racing mode fires several providers in parallel and returns the first response — the right default for any latency-critical path. If you care about fast LLM inference in production, racing across specialized providers is the architectural move that gets you there reliably, not picking one provider and hoping it never queues.
Start with reducing LLM latency to understand every lever in your stack, check the Groq vs Cerebras comparison to pick your first fast-inference provider, and try flo2 free during beta to see what racing across providers actually does to your p95 latency.