Llama 4 inference speed vs Llama 3.3 70B compared

The practical question for teams shipping LLM features is not just model quality but throughput per dollar. Llama 4 vs Llama 3.3 70B speed differences come down to a single architectural fork: Mixture-of-Experts versus a dense 70B transformer. This post breaks down the tradeoffs across six concrete dimensions so you can choose the right backbone without running your own benchmark cluster.

Architecture and Capabilities

Llama 4: Sparse MoE

Meta shipped Llama 4 as a family of sparse mixture-of-experts models. Maverick totals ~400B parameters with 17B active per token; Scout totals ~109B with the same 17B active footprint. The router selects a subset of experts per layer, so the math per generated token resembles a 17B model even though the resident weights are far larger. Both ship with native multimodal ingestion and extended context—1M tokens for Maverick, 10M for Scout. Expert counts are in the 16–128 range depending on release; the gating network runs a top-k selection that adds a small linear cost but avoids full dense compute.

Llama 3.3 70B: Dense and Mature

Llama 3.3 70B is a dense decoder-only transformer with all 70B parameters active on every forward pass. It was tuned to match Llama 3.1 405B quality on many English and code tasks while keeping a single-GPU-friendly(ish) weight count. Context window is 128K tokens, text-only, and the ecosystem around it is frozen—no architectural surprises. There is no image tower, no expert routing, and no variable compute path; what you benchmark is what you get in production.

The capability gap is real: Llama 4 handles images and longer documents natively. But if your workload is pure text and fits in 128K, Llama 3.3 70B still delivers comparable reasoning at a smaller memory surface.

Price and Cost Model

Providers price by token, not by active parameter count. Llama 3.3 70B has a stable, competitive rate because the serving stack is mature and memory-bound on ~140GB of weights (FP8). Llama 4’s larger resident size (Maverick needs far more VRAM even with 17B active) pushes providers to charge a slight premium at launch or restrict batch sizes.

You pay for total memory footprint whether or not all weights compute. That means Llama 4 can be cheaper per computed token but not necessarily per billed token. Watch the metering: a gateway that emits per-token usage logs lets you attribute cost precisely across model switches.

Hidden KV-cache cost

Long context inflates KV-cache memory linearly with sequence length. Llama 4’s 1M–10M window can silently multiply VRAM pressure versus Llama 3.3 70B’s 128K. If you self-host, budget for paged attention and FP8 caches or your effective batch size collapses.

Latency and Throughput

This is where the Llama 4 vs Llama 3.3 70B speed comparison gets interesting.

Decode Speed (Tokens/sec)

Because decode cost scales with active parameters, Llama 4 Maverick generates text at a rate closer to a 17B model than a 70B. On equivalent hardware, expect roughly 2–4x higher tokens/sec for Llama 4 versus Llama 3.3 70B, assuming the provider has optimized the MoE routing kernel. The dense 70B must move and multiply all weights each step, capping single-stream decode.

Time to First Token (TTFT)

TTFT is dominated by prompt processing. Llama 4’s longer context and MoE experts add overhead per input token, but parallel expert compute mitigates it. For prompts under 8K, both models feel similar. For 100K+ prompts, Llama 4 Scout’s 10M context window will incur heavier prefill cost unless the provider uses chunked attention.

Batched Throughput

Under concurrency, Llama 4 wins decisively. The lower active parameter count lets a server pack more sequences per batch before hitting compute limits. Llama 3.3 70B saturates memory bandwidth early. If you front your calls with a gateway like n4n.ai, it honors client routing directives and forwards provider cache-control hints, so you can A/B the Llama 4 vs Llama 3.3 70B speed gap across providers without rewriting app code.

Measuring it yourself

A minimal loop gives you real numbers on your traffic shape:

import time, openai
client = openai.OpenAI(base_url="https://your-gateway/v1", api_key="sk-...")

def bench(model, prompt, max_tokens=128):
    t0 = time.time()
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=max_tokens
    )
    elapsed = time.time() - t0
    gen = resp.usage.completion_tokens
    return gen / elapsed, resp.usage.prompt_tokens

print(bench("meta-llama/llama-3.3-70b-instruct", "Explain Raft."))
print(bench("meta-llama/llama-4-maverick", "Explain Raft."))

Run that against representative prompts; synthetic “write a poem” tests lie.

Ergonomics and API Surface

Both models are exposed via OpenAI-compatible chat completions. The only friction with Llama 4 is multimodal input formatting and the larger max_tokens headroom.

from openai import OpenAI

client = OpenAI(base_url="https://your-gateway/v1", api_key="sk-...")

# Llama 3.3 70B
r1 = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Summarize this log"}],
    temperature=0.2
)

# Llama 4 Maverick (multimodal)
r2 = client.chat.completions.create(
    model="meta-llama/llama-4-maverick",
    messages=[{"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "https://.../chart.png"}},
        {"type": "text", "text": "Extract the trend"}
    ]}],
    temperature=0.2
)

Llama 3.3 70B needs no image handling and works with older client versions. Llama 4 requires providers to implement the vision schema; some gateways still proxy it as text-only until they upgrade. Tool calling is supported on both, but Llama 4’s function-parallelism is less documented—validate your schemas in staging.

Ecosystem and Tooling

Llama 3.3 70B has vLLM, TensorRT-LLM, and llama.cpp paths that are battle-tested. Quantization recipes (Q4_K_M, FP8) are published and reproducible. Llama 4 support is newer: vLLM merged MoE support, but expert parallelism configs are still shifting. If you self-host, budget a week of tuning for Llama 4 versus a day for 3.3.

On managed endpoints, Llama 4’s launch meant sporadic 503s as capacity scaled. n4n.ai provides automatic fallback when a provider is rate-limited, which matters when Llama 4 capacity is constrained at launch—your traffic can shift to Llama 3.3 70B without app changes.

Community adapters (LangChain, LlamaIndex) treat both as drop-in ChatOpenAI subclasses. The difference is that Llama 4 multimodal messages need the beta multimodal flag in some frameworks.

Operational Limits

• Context overflow: Llama 3.3 70B hard-caps at 128K; Llama 4 Scout accepts 10M but most providers clamp at 1M or lower.
• Expert load imbalance: Bad MoE routers can starve GPUs; check provider health metrics.
• Vision cost: Llama 4 image tokens count against context and inflate TTFT.
• Weight memory: Llama 4 Maverick won’t fit on a single 8x80GB node in FP16; plan for FP8 or expert sharding.
• Deprecation risk: Llama 3.3 70B is end-of-line from Meta but stable on providers; Llama 4 will see rapid point releases that may change expert counts.

Head-to-Head Comparison

Which to Choose

Choose Llama 4 if:

• You need native image understanding or >128K context.
• Your traffic is bursty and batch-concurrent; the MoE decode advantage cuts p95 latency.
• You can absorb early ecosystem churn and trust a gateway with fallback.

Choose Llama 3.3 70B if:

• Workloads are text-only, under 128K, and cost-sensitive.
• You self-host and need reproducible quantization today.
• You want zero API surprises; the model is frozen and everywhere.

The Llama 4 vs Llama 3.3 70B speed edge is clear for high-throughput serving, but the dense 70B remains the pragmatic default for stable, cheap text pipelines. Pick based on modality and concurrency, not hype.