Llama 3.3 70B Instruct vs Llama 3.1 70B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Llama 3.3 70B Instruct | Llama 3.1 70B |
|---|---|---|
| Parameters | 70B | 70B |
| Author | Meta | Meta |
| License | Llama 3.3 Community | Llama 3 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 40 GB | 40 GB |
| VRAM at Q5 | 48 GB | 48 GB |
| VRAM at Q8 | 75 GB | 75 GB |
| VRAM at FP16 | 140 GB | 140 GB |
| Use cases | chat, general, reasoning | chat, general |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
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.
About Llama 3.1 70B
Meta's Llama 3.1 70B, the open-weight model that first felt like a credible GPT-4 alternative. Needs serious hardware — think dual 3090s or an A100. Strengths: Benchmark-leading quality for open-weight 70B, 128k context, Strong reasoning and code generation, Mature serving stack in vLLM, TGI, llama.cpp.
How they compare
Llama 3.3 70B Instruct comes from Meta and Llama 3.1 70B 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.
Llama 3.3 70B Instruct and Llama 3.1 70B share the same 70B parameter class. Both need about 40 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
Where they overlap on benchmarks, Llama 3.3 70B Instruct takes HumanEval with 88.4 against 80.5 — a clear 7.9-point margin. They tie on MMLU (86). For workloads weighted toward that benchmark, Llama 3.3 70B Instruct is the stronger default.
For long-context work, Llama 3.1 70B offers the bigger window (128k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, Llama 3.3 70B Instruct requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB, while Llama 3.1 70B requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB. In practice Llama 3.3 70B Instruct 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 3.3 70B Instruct needs roughly 64 GB of system RAM to run on CPU and Llama 3.1 70B 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 6 tokens/sec from Llama 3.3 70B Instruct and 6 from Llama 3.1 70B, scaling up to 20 and 20 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Llama 3.3 70B Instruct: MMLU 86, GPQA Diamond 50.5, HumanEval 88.4.
Reported benchmarks for Llama 3.1 70B: MMLU 86, GPQA 48, HumanEval 80.5.
Bottom line: which should you pick?
- Pick Llama 3.1 70B for long-context work (up to 128k tokens).
- Pick Llama 3.3 70B Instruct if HumanEval performance is your priority (88.4 vs 80.5).
- Pick Llama 3.3 70B Instruct if your workload is reasoning.
Which GPU should you buy to run Llama 3.3 70B Instruct?
To run Llama 3.3 70B Instruct locally at Q4, you need ~40 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Frequently asked questions
What is the difference between Llama 3.3 70B Instruct and Llama 3.1 70B?
The headline differences: both are 70B models; their context windows differ (125k vs 128k tokens); they ship under different licenses (Llama 3.3 Community vs Llama 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 3.3 70B Instruct and Llama 3.1 70B run on a 24 GB GPU?
At a Q4 quantization, Llama 3.3 70B Instruct needs about 40 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 3.1 70B needs about 40 GB and needs more than 24 GB. Both have the same Q4 footprint.
Llama 3.3 70B Instruct vs Llama 3.1 70B for coding — which is better?
On HumanEval, Llama 3.3 70B Instruct leads with 88.4 vs 80.5 (a 7.9-point gap), making it the stronger pick for code generation.
What licenses do Llama 3.3 70B Instruct and Llama 3.1 70B use?
Llama 3.3 70B Instruct is licensed under Llama 3.3 Community and Llama 3.1 70B under Llama 3 Community.
Which has the longer context window, Llama 3.3 70B Instruct or Llama 3.1 70B?
Llama 3.1 70B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.