Llama 4 on Groq: tokens per second vs Together AI

When you are weighing Llama 4 Groq tokens per second vs Together AI, the headline throughput number is only part of the story. Groq’s custom LPU silicon and Together AI’s GPU fleet optimize for different points in the latency–cost–flexibility triangle, and the right choice depends on whether you are building interactive copilots or bulk extraction pipelines. This breakdown compares both providers on concrete dimensions so you can ship without second-guessing your inference backend.

Hardware and serving architecture

Groq serves models on Language Processing Units (LPUs), a deterministic tensor streaming processor designed to eliminate speculative execution and minimize memory-bound stalls. The result is a serving stack where decode latency per token is largely independent of batch size up to the chip’s capacity. Together AI runs on conventional GPU clusters (predominantly H100 and A100) with heavily optimized CUDA kernels, continuous batching, and a distributed router that spreads load across nodes.

That distinction drives everything else. An LPU is a fixed-function pipeline; a GPU is a general matrix engine with scheduling overhead. For a model like Llama 4, which will inherit the MoE and dense variants typical of the family, Groq must compile the graph ahead of time, while Together loads weights into HBM and runs tuned kernels. Both paths are mature, but they fail differently under load.

Tokens per second and latency characteristics

The core search phrase Llama 4 Groq tokens per second vs Together AI is really asking: “who delivers tokens to my user fastest?” Under single-stream decoding—one request, low concurrency—Groq’s LPU typically sustains higher tokens-per-second because there is no kernel launch overhead and no batch scheduler inserting queue delay. Together AI’s GPU stack is competitive but introduces micro-batching latency; even with continuous batching, a lone request shares the SM with housekeeping.

At high concurrency the picture inverts partially. GPUs thrive when many sequences fill the batch; throughput per GPU climbs, and Together’s fleet can scale horizontally behind a load balancer. Groq’s per-chip determinism means adding chips adds capacity linearly, but the total addressable pool of LPUs is smaller than the global GPU supply Together can draw on.

We are not quoting exact benchmarks for Llama 4 because the model is not publicly released at time of writing; any number would be fabrication. Based on the Llama 3 generation on both platforms, Groq led single-stream decode by a clear margin, while Together closed much of the gap at batch sizes above 32. Expect the same relative shape for Llama 4 given similar parameter counts.

Streaming behavior is identical from a client perspective:

from openai import OpenAI

# Groq
groq = OpenAI(base_url="https://api.groq.com/openai/v1", api_key="GROQ_KEY")
stream = groq.chat.completions.create(
    model="llama-4-70b",
    messages=[{"role": "user", "content": "Explain MoE routing."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

# Together AI
together = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER_KEY")
stream = together.chat.completions.create(
    model="meta-llama/Llama-4-70b",
    messages=[{"role": "user", "content": "Explain MoE routing."}],
    stream=True,
)

Both expose OpenAI-compatible streaming, so swapping providers is a base-url and model-string change.

Cost model and metering

Groq prices per output token with a free tier that caps requests per minute—sufficient for prototyping. Paid tiers remove the cap and bill at a flat rate per million tokens. Together AI uses a similar per-token meter but adds two twists: lower per-token rates on reserved capacity (dedicated endpoints) and variable pricing across model sizes. For Llama 4’s larger MoE variant, Together will likely charge more per token than for a dense 8B, whereas Groq’s compilation cost is fixed per model and price differences track weight count less aggressively.

If you front these calls with an OpenAI-compatible gateway such as n4n.ai, you keep per-token usage metering across both backends and can enforce routing directives without rewriting client code.

API ergonomics and SDKs

Both providers mirror the OpenAI chat completion schema. Differences are minor:

• Groq returns system_fingerprint and has strict model-name strings (llama-4-70b).
• Together accepts Hugging Face style IDs (meta-llama/Llama-4-70b) and supports logprobs on more endpoints.

Tool calling and JSON mode are available on both, though Groq’s tool parser has historically lagged Together’s in edge-case strictness. For TypeScript users, the official openai package works unchanged:

import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.together.xyz/v1", apiKey: process.env.TOGETHER });
const res = await client.chat.completions.create({
  model: "meta-llama/Llama-4-70b",
  messages: [{ role: "user", content: "Summarize this PR." }],
});

Ecosystem and model breadth

Groq’s catalog is curated: a handful of Meta models, a few Mistral variants, and proprietary whisper. Together AI hosts 100+ open-weight models, including the full Llama family, Qwen, DeepSeek, and domain fine-tunes. If your system needs to fall back from Llama 4 to a smaller specialist model in the same request path, Together gives you that without cross-provider logic. Groq forces you to step outside its API for non-curated models.

Rate limits and operational guardrails

Groq’s public tier enforces low RPM/TPM ceilings; enterprise contracts raise them but the LPU pool is finite. Together’s limit scales with your spend and dedicated instances bypass shared quotas entirely. For Llama 4, expect Groq to prioritize availability of the flagship variant on day one, while Together may offer more parallel replicas across regions.

Context length follows the model weight release: if Llama 4 ships with 128K context, both will expose it, but Groq’s compile step may delay longest-context support by days. Together typically enables new contexts as soon as weights load.

Head-to-head comparison

Which to choose

Real-time interactive UX (chat, copilots, voice): Pick Groq. The Llama 4 Groq tokens per second vs Together AI gap favors Groq when users are waiting on every token. Deterministic latency makes UI throttling simpler.

High-volume batch or async jobs (ETL, summarization farms): Pick Together AI. GPU batching efficiency and broader fleet capacity win on total cost per million tokens when you can buffer requests.

Multi-model routing in one system: Together AI reduces architectural friction if you need Llama 4 plus smaller models or non-Meta weights in the same client.

Risk-averse production with fallback needs: Use both behind a routing layer. A gateway that honors client routing directives and forwards provider cache-control hints lets you send interactive traffic to Groq and overflow to Together when Groq is rate-limited, without code changes at the app layer.

The Llama 4 Groq tokens per second vs Together AI decision is not about which is universally faster—it is about matching silicon economics to your request shape. Measure your own p95 latency on a representative prompt mix before committing, because the only benchmark that matters is the one hitting your users.