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Llama 3.2 3B Instruct vs Qwen 2.5 3B Instruct

Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.

Quick verdict

Qwen 2.5 3B Instruct is more hardware-efficient — it needs 2.1 GB at Q4_K_M vs 2.8 GB for Llama 3.2 3B Instruct, fitting on 66 GPUs natively.

VRAM at each quantization (8k context)

QuantLlama 3.2 3B InstructQwen 2.5 3B InstructDiff
FP168.2 GB7.3 GB+13%
Q84.6 GB3.8 GB+22%
Q6_K3.7 GB2.9 GB+27%
Q5_K_M3.3 GB2.5 GB+31%
Q4_K_M2.8 GB2.1 GB+37%
Q3_K_M2.5 GB1.7 GB+44%
Q2_K2.1 GB1.4 GB+54%

Diff is Llama 3.2 3B Instruct relative to Qwen 2.5 3B Instruct. Green = lower VRAM (fits more GPUs).

Model specifications

SpecLlama 3.2 3B InstructQwen 2.5 3B Instruct
OrgMetaAlibaba
Parameters3.2B3.1B
ArchitectureDenseDense
Context125k tokens32k tokens
Modalitiestexttext
LicenseLlama 3.2 CommunityQwen Research
CommercialYesNo
Released2024-09-252024-09-19
GPUs (native)66 / 6766 / 67

Benchmark scores

BenchmarkLlama 3.2 3B InstructQwen 2.5 3B Instruct
MMLU-Pro24.032.4
IFEval77.464.0
MATH48.065.9
HumanEval56.774.4

Green = higher score (better). — = not yet available.

GPUs that run only Llama 3.2 3B Instruct(0)

Every GPU that runs Llama 3.2 3B Instruct also runs Qwen 2.5 3B Instruct.

GPUs that run only Qwen 2.5 3B Instruct(0)

Every GPU that runs Qwen 2.5 3B Instruct also runs Llama 3.2 3B Instruct.

GPUs that run both natively(66)

Which should you use?

Choose Llama 3.2 3B Instruct if:
  • • You want maximum capability and have a 3 GB+ GPU
  • • Long context matters — it supports 125k tokens vs 32k
  • • You need commercial use rights
Choose Qwen 2.5 3B Instruct if:
  • • You have limited VRAM — it's a smaller model needing 2.1 GB vs 2.8 GB
  • • Benchmark quality matters — scores 32.4 vs 24.0 on MMLU-Pro

Frequently asked questions

Which is better, Llama 3.2 3B Instruct or Qwen 2.5 3B Instruct?
Llama 3.2 3B Instruct has 3.2B parameters vs 3.1B for Qwen 2.5 3B Instruct, so Llama 3.2 3B Instruct is the larger model. Qwen 2.5 3B Instruct is more hardware-efficient, needing 2.1 GB at Q4_K_M vs 2.8 GB. On MMLU-Pro, Qwen 2.5 3B Instruct scores higher (32.4 vs 24.0).
How much VRAM does Llama 3.2 3B Instruct need vs Qwen 2.5 3B Instruct?
At Q4_K_M quantization with 8k context, Llama 3.2 3B Instruct needs approximately 2.8 GB of VRAM, while Qwen 2.5 3B Instruct needs 2.1 GB. At FP16, Llama 3.2 3B Instruct requires 8.2 GB vs 7.3 GB for Qwen 2.5 3B Instruct.
Can you run Llama 3.2 3B Instruct on the same GPUs as Qwen 2.5 3B Instruct?
Yes, 66 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, no GPU can run Llama 3.2 3B Instruct without also fitting Qwen 2.5 3B Instruct, and no GPU can run Qwen 2.5 3B Instruct without also fitting Llama 3.2 3B Instruct.
What is the difference between Llama 3.2 3B Instruct and Qwen 2.5 3B Instruct?
Llama 3.2 3B Instruct has 3.2B parameters (dense) with a 125k context window. Qwen 2.5 3B Instruct has 3.1B parameters (dense) with a 32k context window. Licensing differs: Llama 3.2 3B Instruct is Llama 3.2 Community while Qwen 2.5 3B Instruct is Qwen Research.
Which model fits in 24 GB of VRAM, Llama 3.2 3B Instruct or Qwen 2.5 3B Instruct?
Both fit in 24 GB of VRAM at Q4_K_M — Llama 3.2 3B Instruct needs 2.8 GB and Qwen 2.5 3B Instruct needs 2.1 GB.
Full Llama 3.2 3B Instruct page →Full Qwen 2.5 3B Instruct page →Check your hardware →