Llama 4 Scout 109B vs Llama 3.3 70B Instruct

The two models at a glance

About Llama 4 Scout 109B

Meta's compact Llama 4 MoE — 109B total, 17B active, natively multimodal, with an unprecedented 10M token context. Fits on a single H100. Strengths: 10M token context — unmatched among open models, Runs on a single H100 thanks to MoE sparsity, Native multimodal input — no separate vision adapter needed, 17B active parameters keeps inference fast.

About Llama 3.3 70B Instruct

Meta's Llama 3.3 70B — same quality tier as Llama 3.1 405B at one-sixth the size, thanks to improved post-training. Weights are gated on Hugging Face. Strengths: Quality competitive with Llama 3.1 405B, 128k context window, Strong reasoning and code performance, Major efficiency gain vs the 405B model.

How they compare

Llama 4 Scout 109B comes from Meta and Llama 3.3 70B Instruct from Meta. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 109B vs 70B parameters, Llama 4 Scout 109B is the larger of the two. At Q4, Llama 3.3 70B Instruct fits in about 40 GB of VRAM versus 65 GB for the other — a 25 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Llama 4 Scout 109B is tuned for chat, general, vision, moe, multilingual, while Llama 3.3 70B Instruct leans toward chat, general, reasoning. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Llama 4 Scout 109B pushes roughly 12 tokens/sec versus 6, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 4 Scout 109B offers the bigger window (9765k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Llama 4 Scout 109B requires Q4 ≈ 65 GB, Q5 ≈ 78 GB, Q8 ≈ 117 GB, FP16 ≈ 218 GB, while Llama 3.3 70B Instruct requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB. In practice Llama 4 Scout 109B spills past 24 GB even at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Llama 4 Scout 109B needs roughly 100 GB of system RAM to run on CPU and Llama 3.3 70B Instruct about 64 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from Llama 4 Scout 109B and 6 from Llama 3.3 70B Instruct, scaling up to 30 and 20 tokens/sec on high-end hardware.

Benchmark scores

Reported benchmarks for Llama 4 Scout 109B: MMLU-Pro 74.

Reported benchmarks for Llama 3.3 70B Instruct: MMLU 86, GPQA Diamond 50.5, HumanEval 88.4.

Bottom line: which should you pick?

• Pick Llama 4 Scout 109B for long-context work (up to 9765k tokens).
• Pick Llama 3.3 70B Instruct for lower VRAM and faster inference; pick Llama 4 Scout 109B for maximum headline quality.
• Pick Llama 4 Scout 109B if your workload is moe, multilingual, vision.
• Pick Llama 3.3 70B Instruct if your workload is reasoning.

Which GPU should you buy to run Llama 4 Scout 109B?

To run Llama 4 Scout 109B locally at Q4, you need ~65 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).

Frequently asked questions

What is the difference between Llama 4 Scout 109B and Llama 3.3 70B Instruct?

The headline differences: Llama 4 Scout 109B is a 109B model and Llama 3.3 70B Instruct is 70B; their context windows differ (9765k vs 125k tokens); they ship under different licenses (Llama 4 Community vs Llama 3.3 Community). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Llama 4 Scout 109B and Llama 3.3 70B Instruct run on a 24 GB GPU?

At a Q4 quantization, Llama 4 Scout 109B needs about 65 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 3.3 70B Instruct needs about 40 GB and needs more than 24 GB. Llama 3.3 70B Instruct is the lighter option for tight VRAM budgets.

Which is faster, Llama 4 Scout 109B or Llama 3.3 70B Instruct?

Llama 3.3 70B Instruct is the smaller model (70B vs 109B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Llama 4 Scout 109B and Llama 3.3 70B Instruct use?

Llama 4 Scout 109B is licensed under Llama 4 Community and Llama 3.3 70B Instruct under Llama 3.3 Community.

Which has the longer context window, Llama 4 Scout 109B or Llama 3.3 70B Instruct?

Llama 4 Scout 109B has the larger context window (9765k vs 125k tokens), so it handles longer documents and codebases in a single prompt.