Faster: Llama 4 Scout More accurate: Llama 3.3 70B * Cheaper: Llama 4 Scout
Llama 4 Scout lands a decisive edge on throughput: on a single A100 it sustains ~210 tok/s per GPU, roughly 1.8× the speed of Llama 3.3 70B under identical settings. The latency drop is most noticeable in real‑time chat and code‑completion loops, where every millisecond counts.
Accuracy tells a different story. With a 70‑billion‑parameter backbone and the refined 3.3 training mix, Llama 3.3 70B consistently outperforms Scout on MMLU (48.7 % vs 44.2 %) and HELM‑Evals (71.3 % vs 66.5 %). The larger context window (8 k vs 4 k) also helps on long‑form generation, reducing drift after the first few paragraphs.
Cost remains the wild card. Scout’s smaller 34 B footprint translates to ~0.45 $/M tokens on Azure, while the 70 B model hovers near 0.78 $/M. For batch inference on static corpora, the price gap can outweigh the modest quality gain.
Verdict: Choose Llama 4 Scout for latency‑sensitive, high‑volume workloads where budget constraints dominate. Opt for Llama 3.3 70B when precision on complex reasoning or long‑context tasks is non‑negotiable.
| Scenario | Recommended model | Reason |
|---|---|---|
| Real‑time chat bots | Llama 4 Scout | Highest token‑per‑second throughput |
| Code completion (IDE) | Llama 4 Scout | Low latency, acceptable accuracy |
| Academic research papers | Llama 3.3 70B | Superior long‑context coherence |
| Knowledge‑intensive QA | Llama 3.3 70B | Better reasoning and factuality |
| Large‑scale batch embeddings | Llama 4 Scout | Lower per‑token cost |
# Quick benchmark: compare tok/s on a single A100
python -m lm_eval.run \
--model llama4_scout --tasks hellaswag \
--max_seq_len 4096 --batch_size 8Llama 4 Scout consistently outpaces Llama 3.3 70B on raw throughput. On a single NVIDIA A100 (40 GB, FP16) it delivers ≈210 tokens / s versus ≈115 tokens / s for the 70 B model—a 1.8× speed gain that translates into sub‑10 ms response times in a typical 256‑token chat turn. The latency advantage is most evident in low‑batch, real‑time scenarios such as code‑completion IDE plugins, where Scout’s optimized attention kernel reduces per‑token overhead by ~30 µs.
Accuracy, however, still favors the larger backbone. On the MMLU 5‑shot benchmark Scout scores 71.2 %, while Llama 3.3 70B reaches 74.8 %. The gap widens on code‑generation: HumanEval pass@1 is 38 % for Scout versus 45 % for the 70 B model. These differences stem from the richer parameter space and the more diverse pre‑training mix of Llama 3.3 70B, which better captures nuanced reasoning patterns.
Verdict: Choose Scout when latency and cost dominate (interactive chat, edge inference, high‑throughput batch jobs). Opt for Llama 3.3 70B when raw correctness matters (research prototyping, complex reasoning, code synthesis).
| Use‑case | Recommended model | Reasoning |
|---|---|---|
| Real‑time customer support | Llama 4 Scout | Lowest latency, cheaper per token |
| IDE code‑completion | Llama 4 Scout | Faster token emission, acceptable accuracy |
| Academic QA / tutoring | Llama 3.3 70B | Higher MMLU score, better factual recall |
| Automated code generation | Llama 3.3 70B | Superior HumanEval pass rates |
# Quick reproducible benchmark (single GPU, 256‑token prompt)
python run_benchmark.py \
--model llama4-scout \
--prompt_len 256 \
--batch_size 1 \
--device a100Llama 4 Scout adopts a “speed‑first” transformer stack. It trims the depth to 48 layers but widens each layer to 16 k hidden units, and introduces grouped‑query attention (GQA) with a 4‑head query group. The model also leverages FlashAttention‑2 and a mixed‑precision pipeline that keeps activations in bfloat16 while storing weights in 4‑bit NF4. By contrast, Llama 3.3 70B sticks to the classic dense design: 80 layers, 12 k hidden size, full‑query‑key‑value attention, and FP16‑only weights. The extra depth gives it a richer representational capacity, but the lack of GQA and kernel‑level optimizations makes it slower per token.
Training data diverges sharply. Scout was exposed to a curated 1.2 trillion‑token mix that leans 30 % toward recent code repositories, 25 % multilingual web text (up to 100 languages), and 45 % high‑quality instruction data filtered through a dual‑ranker pipeline. Llama 3.3 70B used a 1.0 trillion‑token corpus dominated by English web crawl (≈65 %), with only 15 % code and a modest 20 % instruction set, and its filtering relied on a single‑stage heuristic. The newer, more diverse token stream gives Scout a modest edge on programming and multilingual prompts, while the larger, more homogeneous dataset preserves Llama 3.3 70B’s raw factual recall.
| Feature | Llama 4 Scout | Llama 3.3 70B |
|---|---|---|
| Layers / Hidden size | 48 × 16 k | 80 × 12 k |
| Attention type | Grouped‑Query (4‑head) | Full‑Query‑Key‑Value |
| Weight precision | 4‑bit NF4 + BF16 activations | FP16 weights |
| Training tokens | 1.2 T (30 % code, 25 % multilingual) | 1.0 T (15 % code, 20 % instruction) |
| Primary strength | Throughput & low‑latency inference | Accuracy & factual depth |
Llama 4 Scout’s 4‑bit NF4 weight format cuts VRAM demand to roughly 30 GB for the full model, letting a single 40 GB A100 host the entire checkpoint with headroom for batch buffers. By contrast, Llama 3.3 70B remains a 16‑bit FP16 beast, requiring the full 70 GB of memory; you need either a 80 GB H100 or model‑parallel sharding across two A100s. The memory savings translate directly into lower cloud spend: on major providers a single‑GPU A100 instance costs ≈ $2.80 /hr, while a dual‑GPU A100‑80 GB setup tops $5.60 /hr.
Throughput scales linearly for Scout up to eight A100s, reaching ~1.6 k tok/s with a modest 0.9× cost per token relative to the 70 B model, which only gains ~1.3× speed when you double the GPU count because of inter‑GPU communication overhead. In practice, Scout can sustain real‑time inference on a 4‑GPU node for a 1 M‑token daily workload at under $0.02 per 1 k tokens, whereas the 70 B model climbs to $0.04 per 1 k tokens even on a 2‑node cluster.
Verdict: Choose Scout for cost‑sensitive, high‑throughput services (chat bots, code completion, batch summarisation). Reserve the 70 B model for tasks where raw accuracy outweighs compute budget (research‑grade reasoning, complex multi‑turn dialogues).
| Use‑case | Preferred model | Reasoning |
|---|---|---|
| Real‑time chat (≤ 256 tok) | Scout | Sub‑10 ms latency, fits on one A100 |
| Large‑scale batch summarisation | Scout | Cheapest token‑price, linear scaling |
| Deep reasoning / chain‑of‑thought | 3.3 70B | Higher parameter count improves depth |
| Multi‑modal research prototypes | 3.3 70B | Better accuracy justifies extra cost |
Real‑time interactive applications—chat assistants, IDE code‑completion, and low‑latency retrieval‑augmented generation—benefit from Llama 4 Scout’s throughput‑first design. Its 210 tok/s per A100 and 4‑bit NF4 weight format keep memory footprints low enough to run multiple instances on a single GPU, making it ideal for SaaS providers that must serve thousands of concurrent users without scaling hardware.
Batch‑oriented workloads that can tolerate higher latency but demand the highest possible accuracy fit Llama 3.3 70B. The 70 B parameter count and richer training mix give it a measurable edge on complex reasoning, multi‑step planning, and domain‑specific knowledge extraction. Enterprises that run nightly analytics, generate long‑form reports, or fine‑tune on niche corpora will see better output quality despite the slower 115 tok/s throughput.
| Scenario | Preferred Model | Why |
|---|---|---|
| Live chat / voice assistants | Llama 4 Scout | Highest token‑per‑second rate, low memory |
| IDE autocomplete / code‑completion | Llama 4 Scout | Sub‑10 ms latency, cheap multi‑instance deployment |
| Long‑form content generation | Llama 3.3 70B | Superior reasoning, richer factual recall |
| Batch QA / document summarization | Llama 3.3 70B | Better accuracy on multi‑step tasks |
| Fine‑tuning on proprietary data | Llama 3.3 70B | Larger capacity handles niche vocabularies |
Llama 4 Scout is the clear choice when latency and cost dominate the equation. Its 4‑bit NF4 weight format and FlashAttention‑2 pipeline keep GPU memory low, letting you run two instances on a single A100 for roughly the same price as one 70 B model. In batch‑size‑1 chat or IDE‑integrated code completion, the sub‑10 ms turnaround feels noticeably snappier than the 70 B’s 20 ms lag.
If raw reasoning depth, factual recall, or nuanced instruction following matters more than raw speed, Llama 3.3 70B still leads. The larger context window (8 k tokens vs 4 k) and the richer training mix give it a measurable edge on MMLU and code‑generation benchmarks, especially for multi‑step problem solving. For research prototyping or any workload that can tolerate higher latency, the 70 B’s superior accuracy justifies the extra GPU hours.
Verdict:
| Use‑case | Recommended model | Reasoning |
|---|---|---|
| Live chat assistants | Llama 4 Scout | Lowest latency, cheaper scaling |
| IDE code‑completion plugins | Llama 4 Scout | Sub‑10 ms response, fits on single GPU |
| Academic research (papers) | Llama 3.3 70B | Higher token limit, better factual recall |
| Multi‑turn reasoning tasks | Llama 3.3 70B | Superior accuracy on chain‑of‑thought |
| Large‑scale batch inference | Llama 3.3 70B | Better per‑token quality, tolerates latency |
Both models excel in distinct niches: Llama 4 Scout wins on raw speed and cost, while Llama 3.3 70B still leads on nuanced reasoning and domain‑specific accuracy.
| Metric | Llama 4 Scout | Llama 3.3 70B |
|---|---|---|
| Parameters | 48 B (4‑bit NF4) | 70 B (FP16) |
| Throughput (A100, FP16) | ~210 tok/s | ~115 tok/s |
| 256‑token latency (ms) | 9 ms | 16 ms |
| Peak VRAM usage | 18 GB | 30 GB |
| Inference cost ($/M tok) | 0.018 | 0.032 |
| Win‑rate on MMLU (avg.) | 71 % | 78 % |
| Code‑completion score (HumanEval) | 48 % | 55 % |
| Ideal Use‑Case | Recommended Model |
|---|---|
| Interactive assistants (sub‑10 ms) | Llama 4 Scout |
| Large‑scale document summarisation | Llama 4 Scout |
| Academic research & reasoning | Llama 3.3 70B |
| Specialized coding tools | Llama 3.3 70B (for higher correctness) |
Llama 4 Scout builds on the same transformer backbone as the 3.3 series but introduces a hybrid dense‑sparse routing layer, a larger context window (64K tokens vs 32K), and a revamped positional encoding that reduces attention drift. It also uses a more aggressive mixture‑of‑experts (MoE) gating with 4‑way expert selection, whereas Llama 3.3 70B is a pure dense model. These changes give Scout higher capacity per parameter, better scaling for long‑form tasks, and a modest reduction in FLOPs per token despite the larger model size.
On a single NVIDIA H100 GPU, Llama 4 Scout typically runs about 12‑15% slower per token than Llama 3.3 70B because the MoE routing adds overhead and the longer context window requires more KV‑cache memory. However, when the model is sharded across two H100s, the parallel MoE execution can actually reduce latency, making Scout roughly on‑par or slightly faster for batches larger than 8 tokens. CPU‑only inference remains significantly slower for both models, with Scout's extra routing logic adding another 20‑30 ms per batch.
Llama 4 Scout is generally more cost‑effective for high‑throughput workloads despite its higher per‑token latency. Its MoE design yields a 1.4× higher token‑level quality per dollar when using the recommended mixed‑precision (FP8) mode, because the model achieves the same downstream accuracy with fewer total tokens. Llama 3.3 70B, being a dense model, consumes more GPU memory per token, which can increase the number of GPUs needed for parallel generation. For low‑volume or latency‑critical applications, 3.3 70B may be cheaper, but for bulk generation at scale, Scout’s efficiency wins.
In the latest MMLU benchmark, Llama 4 Scout scores 84.2% average accuracy, surpassing Llama 3.3 70B's 78.5% by about 5.7 points, which translates to a noticeable edge on reasoning‑heavy categories such as law and STEM. HELM's weighted performance metric places Scout roughly 0.12 points ahead, reflecting its superior few‑shot capabilities. However, on pure latency‑focused tasks like code completion, the gap narrows because the routing overhead counters quality gains. Overall, the benchmarks confirm that Scout delivers a consistent 5‑10% quality uplift across most language tasks.
Yes. Scout’s MoE layers require a different optimizer schedule; the recommended setup uses AdamW with a warm‑up phase of 2 % of total steps followed by a cosine decay, and a gating‑loss weight of 0.01 to keep expert utilization balanced. Existing dense checkpoints from Llama 3.3 70B cannot be directly loaded— you must start from a Scout‑specific pretrained checkpoint. Tokenizer versions are compatible, but the longer context window may need adjustment of positional‑embedding caches in your data pipeline. Finally, inference APIs expose an extra "expert‑mask" flag that should be set when serving the model.