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MODEL FAMILY COMPARISON

Llama vs Qwen: which model family should you try?

Both families have broad Hugging Face ecosystems. The better choice depends on language coverage, toolchain support, license terms, model size and the workload you can actually test.

Decision summary

Broad community ecosystemLlama has extensive tutorials, fine-tunes and deployment integrations.
Multilingual and Chinese workloadsQwen is commonly shortlisted, but evaluate your own language and domain data.
Commercial deploymentReview the exact repository license and acceptable-use terms for either family.
Limited GPU memoryCompare the exact parameter size and quantization, not only the family name.

Compare capabilities, not branding

Model-family labels cover many base, instruct, code, vision, audio and quantized variants. Choose candidates that match the task first, then compare evaluation evidence, context requirements, runtime compatibility and memory use.

Popular Llama results in the index

text-generationGated

meta-llama/Llama-3.2-1B-Instruct

meta-llama/Llama-3.2-1B-Instruct is a text generation model indexed for deployment research. Estimated minimum GPU memory is 6 GB. Access approval is required on Hugging Face.

6,107,1961,611
1.0B6 GB+ VRAMllama3.2
Deployment details
text-generationGated

meta-llama/Llama-3.1-8B-Instruct

meta-llama/Llama-3.1-8B-Instruct is a text generation model indexed for deployment research. Estimated minimum GPU memory is 24 GB. Access approval is required on Hugging Face.

5,644,0396,837
8.0B24 GB+ VRAMllama3.1
Deployment details
text-generation

dphn/dolphin-2.9.1-yi-1.5-34b

dphn/dolphin-2.9.1-yi-1.5-34b is a text generation model indexed for deployment research. Estimated minimum GPU memory is 80 GB. It is publicly listed on Hugging Face.

4,769,15265
34.0B80 GB+ VRAMapache-2.0
Deployment details
text-classificationGated

meta-llama/Prompt-Guard-86M

meta-llama/Prompt-Guard-86M is a text classification model indexed for deployment research. Estimated minimum GPU memory is 8 GB. Access approval is required on Hugging Face.

4,494,080397
Unknown8 GB+ VRAMllama3.1
Deployment details
text-generation

HuggingFaceTB/SmolLM2-135M

HuggingFaceTB/SmolLM2-135M is a text generation model indexed for deployment research. Estimated minimum GPU memory is 8 GB. It is publicly listed on Hugging Face.

2,550,777230
Unknown8 GB+ VRAMapache-2.0
Deployment details

Popular Qwen results in the index

text-generation

Qwen/Qwen3-0.6B

Qwen/Qwen3-0.6B is a text generation model indexed for deployment research. Estimated minimum GPU memory is 4 GB. It is publicly listed on Hugging Face.

21,493,0271,588
600M4 GB+ VRAMapache-2.0
Deployment details
image-text-to-text

Qwen/Qwen3-VL-8B-Instruct

Qwen/Qwen3-VL-8B-Instruct is a image text to text model indexed for deployment research. Estimated minimum GPU memory is 24 GB. It is publicly listed on Hugging Face.

15,231,4941,084
8.0B24 GB+ VRAMapache-2.0
Deployment details
text-generation

Qwen/Qwen3-8B

Qwen/Qwen3-8B is a text generation model indexed for deployment research. Estimated minimum GPU memory is 24 GB. It is publicly listed on Hugging Face.

13,046,8291,356
8.0B24 GB+ VRAMapache-2.0
Deployment details
image-text-to-text

Qwen/Qwen3.6-35B-A3B-FP8

Qwen/Qwen3.6-35B-A3B-FP8 is a image text to text model indexed for deployment research. Estimated minimum GPU memory is 80 GB. It is publicly listed on Hugging Face.

12,372,077376
35.0B80 GB+ VRAMapache-2.0
Deployment details
image-text-to-text

Qwen/Qwen3.5-9B

Qwen/Qwen3.5-9B is a image text to text model indexed for deployment research. Estimated minimum GPU memory is 24 GB. It is publicly listed on Hugging Face.

11,389,2111,912
9.0B24 GB+ VRAMapache-2.0
Deployment details

A fair evaluation workflow

  1. Select models with similar parameter counts and intended task.
  2. Use the same prompt set, decoding settings and output-quality rubric.
  3. Measure latency, throughput and peak GPU memory on the same hardware.
  4. Test the languages, safety cases and structured outputs your product needs.
  5. Record the exact revision, quantization and runtime before deciding.

Popularity and download counts are discovery signals, not proof that one family is better. AI Pentium is independent and is not affiliated with Meta, Alibaba or Hugging Face.

Compare selected models side by side