Mistral Nemo 12B Instruct vs Llama 3.1 8B

The two models at a glance

About Mistral Nemo 12B Instruct

Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage. Strengths: 128k context window, Strong European multilingual performance, Apache 2.0 license, Efficient Tekken tokenizer reduces token counts.

About Llama 3.1 8B

Meta's Llama 3.1 8B, the open-weight benchmark of 2024. A 128k context, well-behaved instruction follower with the largest ecosystem in the open-source world. Strengths: 128k context window, Strong instruction following and coding, Enormous ecosystem of fine-tunes and integrations, Solid quality-to-size ratio.

How they compare

Mistral Nemo 12B Instruct comes from Mistral AI and Llama 3.1 8B from Meta, they belong to the Mistral and Llama families respectively. 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 12B vs 8B parameters, Mistral Nemo 12B Instruct is the larger of the two. At Q4, Llama 3.1 8B fits in about 6 GB of VRAM versus 7 GB for the other — a 1 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Llama 3.1 8B takes MMLU with 73 against 68 — a clear 5-point margin. For workloads weighted toward that benchmark, Llama 3.1 8B is the stronger default.

On a typical mid-range GPU, Llama 3.1 8B pushes roughly 30 tokens/sec versus 25, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 3.1 8B offers the bigger window (128k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Mistral Nemo 12B Instruct requires Q4 ≈ 7 GB, Q5 ≈ 9 GB, Q8 ≈ 13 GB, FP16 ≈ 24 GB, while Llama 3.1 8B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB. In practice Mistral Nemo 12B Instruct fits an 8 GB card 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, Mistral Nemo 12B Instruct needs roughly 16 GB of system RAM to run on CPU and Llama 3.1 8B about 10 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 25 tokens/sec from Mistral Nemo 12B Instruct and 30 from Llama 3.1 8B, scaling up to 70 and 80 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Mistral Nemo 12B Instruct or Llama 3.1 8B to the card you actually own:

• On a 8 GB GPU: Mistral Nemo 12B Instruct runs at Q4 (7 GB); Llama 3.1 8B runs at Q5 (7 GB).
• On a 12 GB GPU: Mistral Nemo 12B Instruct runs at Q5 (9 GB); Llama 3.1 8B runs at Q8 (10 GB).
• On a 16 GB GPU: Mistral Nemo 12B Instruct runs at Q8 (13 GB); Llama 3.1 8B runs at Q8 (10 GB).
• On a 24 GB GPU: Mistral Nemo 12B Instruct runs at FP16 (24 GB); Llama 3.1 8B runs at FP16 (18 GB).

Benchmark scores

Reported benchmarks for Mistral Nemo 12B Instruct: MMLU 68, HellaSwag 83.5, Winogrande 76.8.

Reported benchmarks for Llama 3.1 8B: MMLU 73, HumanEval 72.6, GPQA 46.7.

Bottom line: which should you pick?

• Pick Mistral Nemo 12B Instruct if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Llama 3.1 8B for long-context work (up to 128k tokens).
• Pick Llama 3.1 8B for lower VRAM and faster inference; pick Mistral Nemo 12B Instruct for maximum headline quality.
• Pick Llama 3.1 8B if MMLU performance is your priority (73 vs 68).
• Pick Mistral Nemo 12B Instruct if your workload is fr, multilingual.

Which GPU should you buy to run Mistral Nemo 12B Instruct?

To run Mistral Nemo 12B Instruct locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Mistral Nemo 12B Instruct and Llama 3.1 8B?

The headline differences: Mistral Nemo 12B Instruct is a 12B model and Llama 3.1 8B is 8B; their context windows differ (125k vs 128k tokens); they ship under different licenses (Apache 2.0 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 Mistral Nemo 12B Instruct and Llama 3.1 8B run on a 24 GB GPU?

At a Q4 quantization, Mistral Nemo 12B Instruct needs about 7 GB of VRAM and fits comfortably on a 24 GB GPU; Llama 3.1 8B needs about 6 GB and fits comfortably on a 24 GB GPU. Llama 3.1 8B is the lighter option for tight VRAM budgets.

Is Mistral Nemo 12B Instruct or Llama 3.1 8B more capable?

On MMLU, Llama 3.1 8B scores higher (73 vs 68), a 5-point advantage on this benchmark.

Which is faster, Mistral Nemo 12B Instruct or Llama 3.1 8B?

Llama 3.1 8B is the smaller model (8B vs 12B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Mistral Nemo 12B Instruct or Llama 3.1 8B?

Mistral Nemo 12B Instruct ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 3 Community — check its terms before commercial deployment.

Which has the longer context window, Mistral Nemo 12B Instruct or Llama 3.1 8B?

Llama 3.1 8B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.