Llama 3.1 8B vs Qwen 2.5 7B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Llama 3.1 8B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Author | Meta | Alibaba |
| License | Llama 3 Community | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 6 GB | 5 GB |
| VRAM at Q5 | 7 GB | 6 GB |
| VRAM at Q8 | 10 GB | 9 GB |
| VRAM at FP16 | 18 GB | 16 GB |
| Use cases | chat, general | chat, general, multilingual |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
For unambiguous commercial use, Qwen 2.5 7B has the safer license (Apache 2.0) compared to Llama 3 Community.
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 Qwen 2.5 7B
Alibaba's Qwen 2.5 7B, a top-tier 7B for its era with a 128k context, strong multilingual coverage across 29 languages, and Apache 2.0 licensing. Strengths: 128k context window, Apache 2.0 license with no MAU restrictions, Strong multilingual performance across 29 languages, Better math and coding than Llama 3.1 8B at the same size.
How they compare
Llama 3.1 8B comes from Meta and Qwen 2.5 7B from Alibaba, they belong to the Llama and Qwen 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 7B parameters, Llama 3.1 8B is the larger of the two. At Q4, Qwen 2.5 7B fits in about 5 GB of VRAM versus 6 GB for the other — a 1 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Qwen 2.5 7B takes HumanEval with 84.8 against 72.6 — a decisive 12.2-point margin. On MMLU the edge goes to Qwen 2.5 7B (74.2 vs 73). For workloads weighted toward that benchmark, Qwen 2.5 7B is the stronger default.
On a typical mid-range GPU, Qwen 2.5 7B pushes roughly 35 tokens/sec versus 30, so it is the more responsive choice for interactive or high-volume use.
Memory, quantization & throughput
Across quantization levels, Llama 3.1 8B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB, while Qwen 2.5 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 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 Qwen 2.5 7B about 8 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 35 from Qwen 2.5 7B, scaling up to 80 and 90 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 Qwen 2.5 7B to the card you actually own:
- On a 8 GB GPU: Llama 3.1 8B runs at Q5 (7 GB); Qwen 2.5 7B runs at Q5 (6 GB).
- On a 12 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Qwen 2.5 7B runs at Q8 (9 GB).
- On a 16 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Qwen 2.5 7B runs at FP16 (16 GB).
- On a 24 GB GPU: Llama 3.1 8B runs at FP16 (18 GB); Qwen 2.5 7B runs at FP16 (16 GB).
Benchmark scores
Reported benchmarks for Llama 3.1 8B: MMLU 73, HumanEval 72.6, GPQA 46.7.
Reported benchmarks for Qwen 2.5 7B: MMLU 74.2, HumanEval 84.8, MATH 75.5.
Bottom line: which should you pick?
- Pick Qwen 2.5 7B if you need a permissive (Apache 2.0) license for commercial deployment.
- Pick Qwen 2.5 7B for lower VRAM and faster inference; pick Llama 3.1 8B for maximum headline quality.
- Pick Qwen 2.5 7B if HumanEval performance is your priority (84.8 vs 72.6).
- Pick Qwen 2.5 7B if your workload is multilingual.
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).
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Frequently asked questions
What is the difference between Llama 3.1 8B and Qwen 2.5 7B?
The headline differences: Llama 3.1 8B is a 8B model and Qwen 2.5 7B is 7B; they ship under different licenses (Llama 3 Community vs Apache 2.0). 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 Qwen 2.5 7B 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; Qwen 2.5 7B needs about 5 GB and fits comfortably on a 24 GB GPU. Qwen 2.5 7B is the lighter option for tight VRAM budgets.
Llama 3.1 8B vs Qwen 2.5 7B for coding — which is better?
On HumanEval, Qwen 2.5 7B leads with 84.8 vs 72.6 (a 12.2-point gap), making it the stronger pick for code generation.
Which is faster, Llama 3.1 8B or Qwen 2.5 7B?
Qwen 2.5 7B is the smaller model (7B vs 8B), 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, Llama 3.1 8B or Qwen 2.5 7B?
Qwen 2.5 7B 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.