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Qwen 2.5 Coder 32B Instruct vs Llama 3.3 70B Instruct

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

Quick verdict

Qwen 2.5 Coder 32B Instruct is more hardware-efficient — it needs 20.6 GB at Q4_K_M vs 42.2 GB for Llama 3.3 70B Instruct, fitting on 51 GPUs natively.

VRAM at each quantization (8k context)

QuantQwen 2.5 Coder 32B InstructLlama 3.3 70B InstructDiff
FP1675.2 GB159.8 GB-53%
Q838.8 GB81.4 GB-52%
Q6_K29.7 GB61.8 GB-52%
Q5_K_M25.2 GB52.0 GB-52%
Q4_K_M20.6 GB42.2 GB-51%
Q3_K_M17.0 GB34.4 GB-51%
Q2_K13.3 GB26.5 GB-50%

Diff is Qwen 2.5 Coder 32B Instruct relative to Llama 3.3 70B Instruct. Green = lower VRAM (fits more GPUs).

Model specifications

SpecQwen 2.5 Coder 32B InstructLlama 3.3 70B Instruct
OrgAlibabaMeta
Parameters32.5B70B
ArchitectureDenseDense
Context125k tokens125k tokens
Modalitiestexttext
LicenseApache 2.0Llama 3.3 Community
CommercialYesYes
Released2024-11-122024-12-06
GPUs (native)51 / 6738 / 67

Benchmark scores

BenchmarkQwen 2.5 Coder 32B InstructLlama 3.3 70B Instruct
MMLU-Pro50.468.9
HumanEval92.788.4
MATH62.077.0

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

GPUs that run only Qwen 2.5 Coder 32B Instruct(13)

GPUs that run only Llama 3.3 70B Instruct(0)

Every GPU that runs Llama 3.3 70B Instruct also runs Qwen 2.5 Coder 32B Instruct.

GPUs that run both natively(38)

Which should you use?

Choose Qwen 2.5 Coder 32B Instruct if:
  • • You have limited VRAM — it's a smaller model needing 20.6 GB vs 42.2 GB
  • • You're running coding tasks
Choose Llama 3.3 70B Instruct if:
  • • You want maximum capability and have a 43 GB+ GPU
  • • Benchmark quality matters — scores 68.9 vs 50.4 on MMLU-Pro

Frequently asked questions

Which is better, Qwen 2.5 Coder 32B Instruct or Llama 3.3 70B Instruct?
Qwen 2.5 Coder 32B Instruct has 32.5B parameters vs 70B for Llama 3.3 70B Instruct, so Llama 3.3 70B Instruct is the larger model. Qwen 2.5 Coder 32B Instruct is more hardware-efficient, needing 20.6 GB at Q4_K_M vs 42.2 GB. Qwen 2.5 Coder 32B Instruct runs on more GPUs natively (51 vs 38). On MMLU-Pro, Llama 3.3 70B Instruct scores higher (68.9 vs 50.4).
How much VRAM does Qwen 2.5 Coder 32B Instruct need vs Llama 3.3 70B Instruct?
At Q4_K_M quantization with 8k context, Qwen 2.5 Coder 32B Instruct needs approximately 20.6 GB of VRAM, while Llama 3.3 70B Instruct needs 42.2 GB. At FP16, Qwen 2.5 Coder 32B Instruct requires 75.2 GB vs 159.8 GB for Llama 3.3 70B Instruct.
Can you run Qwen 2.5 Coder 32B Instruct on the same GPUs as Llama 3.3 70B Instruct?
Yes, 38 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA H100 80GB, NVIDIA A100 80GB. However, 13 GPUs can run Qwen 2.5 Coder 32B Instruct but not Llama 3.3 70B Instruct, and no GPU can run Llama 3.3 70B Instruct without also fitting Qwen 2.5 Coder 32B Instruct.
What is the difference between Qwen 2.5 Coder 32B Instruct and Llama 3.3 70B Instruct?
Qwen 2.5 Coder 32B Instruct has 32.5B parameters (dense) with a 125k context window. Llama 3.3 70B Instruct has 70B parameters (dense) with a 125k context window. Licensing differs: Qwen 2.5 Coder 32B Instruct is Apache 2.0 while Llama 3.3 70B Instruct is Llama 3.3 Community.
Which model fits in 24 GB of VRAM, Qwen 2.5 Coder 32B Instruct or Llama 3.3 70B Instruct?
Only Qwen 2.5 Coder 32B Instruct fits in 24 GB at Q4_K_M (20.6 GB). Llama 3.3 70B Instruct needs 42.2 GB, requiring a larger GPU.
Full Qwen 2.5 Coder 32B Instruct page →Full Llama 3.3 70B Instruct page →Check your hardware →