Llama 3.1 8B vs Gemma 2 9B

The two models at a glance

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.

About Gemma 2 9B

Google's Gemma 2 9B, a distilled instruct model that outperforms Llama 3 8B on several benchmarks at a slightly larger size. Strengths: Beats Llama 3 8B on multiple benchmarks, Solid quality-per-parameter, Reliable instruction following, Distilled from Gemma 2 27B for better quality density.

How they compare

Llama 3.1 8B comes from Meta and Gemma 2 9B from Google, they belong to the Llama and Gemma 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 8B vs 9B parameters, Gemma 2 9B is the larger of the two. Both need about 6 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.

Where they overlap on benchmarks, Llama 3.1 8B takes HumanEval with 72.6 against 40.2 — a decisive 32.4-point margin. On MMLU the edge goes to Llama 3.1 8B (73 vs 71.3). 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 28, 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 8k tokens).

Memory, quantization & throughput

Across quantization levels, Llama 3.1 8B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB, while Gemma 2 9B requires Q4 ≈ 6 GB, Q5 ≈ 7.5 GB, Q8 ≈ 11 GB, FP16 ≈ 20 GB. In practice Llama 3.1 8B 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, Llama 3.1 8B needs roughly 10 GB of system RAM to run on CPU and Gemma 2 9B about 12 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 30 tokens/sec from Llama 3.1 8B and 28 from Gemma 2 9B, scaling up to 80 and 75 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 Llama 3.1 8B or Gemma 2 9B to the card you actually own:

• On a 8 GB GPU: Llama 3.1 8B runs at Q5 (7 GB); Gemma 2 9B runs at Q5 (7.5 GB).
• On a 12 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Gemma 2 9B runs at Q8 (11 GB).
• On a 16 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Gemma 2 9B runs at Q8 (11 GB).
• On a 24 GB GPU: Llama 3.1 8B runs at FP16 (18 GB); Gemma 2 9B runs at FP16 (20 GB).

Benchmark scores

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

Reported benchmarks for Gemma 2 9B: MMLU 71.3, HellaSwag 87.2, HumanEval 40.2.

Bottom line: which should you pick?

• 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 Gemma 2 9B for maximum headline quality.
• Pick Llama 3.1 8B if HumanEval performance is your priority (72.6 vs 40.2).

Which GPU should you buy to run Llama 3.1 8B?

To run Llama 3.1 8B locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Llama 3.1 8B and Gemma 2 9B?

The headline differences: Llama 3.1 8B is a 8B model and Gemma 2 9B is 9B; their context windows differ (128k vs 8k tokens); they ship under different licenses (Llama 3 Community vs Gemma). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Llama 3.1 8B and Gemma 2 9B run on a 24 GB GPU?

At a Q4 quantization, Llama 3.1 8B needs about 6 GB of VRAM and fits comfortably on a 24 GB GPU; Gemma 2 9B needs about 6 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.

Llama 3.1 8B vs Gemma 2 9B for coding — which is better?

On HumanEval, Llama 3.1 8B leads with 72.6 vs 40.2 (a 32.4-point gap), making it the stronger pick for code generation.

Which is faster, Llama 3.1 8B or Gemma 2 9B?

Llama 3.1 8B is the smaller model (8B vs 9B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Llama 3.1 8B and Gemma 2 9B use?

Llama 3.1 8B is licensed under Llama 3 Community and Gemma 2 9B under Gemma.

Which has the longer context window, Llama 3.1 8B or Gemma 2 9B?

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