Key Highlights
Core Difference: Gemma 3 27B is a versatile and efficient multimodal model, capable of processing both images and text. Llama 3.3 70B is a larger, text-only powerhouse optimized for complex reasoning and instruction-following tasks.
Performance: Llama 3.3 70B generally leads in text-centric benchmarks for coding, instruction following, and general knowledge. Gemma 3 27B shows strong performance in math and offers the unique advantage of visual understanding.
Hardware Accessibility: Gemma 3 27B is engineered for efficiency and is touted as one of the most capable models that can run on a single high-end GPU, making it more accessible for local deployment. Llama 3.3 70B’s larger size demands more substantial hardware, often requiring multi-GPU setups.
Best For: Choose Gemma 3 27B for applications requiring multimodality, broad language support, and efficient deployment on constrained hardware. Opt for Llama 3.3 70B for enterprise-grade, text-heavy applications where top-tier performance are critical.
Google’s Gemma 3 27B and Meta’s Llama 3.3 70B are top open-source AI models. This quick guide compares their strengths so you can pick the right one for your project—fast.
Basic Introduction: Gemma 3 27B vs. Llama 3.3 70B
Let’s start with a foundational look at what sets these two models apart.
| Feature | Gemma 3 27B | Llama 3.3 70B |
|---|---|---|
| Developer | Meta | |
| Release Date | March 12, 2025 | December 6, 2024 |
| Parameters | 27 Billion | 70 Billion |
| Modality | Multimodal (Image & Text Input) | Text-Only |
| Architecture | Interleaved Local-Global Attention | Optimized Transformer with GQA |
| Training Data | 14 Trillion Tokens | Over 15 Trillion Tokens |
| Context Window | 128,000 Tokens | 128,000 Tokens |
| Multilingual | Supports over 140 languages | Official support for 8 languages |
| Expansion | Structured Outputs, Function Calling with Langchain | Function Calling |
Gemma 3’s standout feature is its multimodality, allowing it to interpret visual information alongside text. Llama 3.3 70B, while text-only, is more than double the size in parameter count, which often translates to more nuanced and powerful text generation and reasoning capabilities.
Performance: A Tale of Two Specializations
| Benchmark | Gemma 3 27B | Llama 3․3 70B |
|---|---|---|
| MMLU-Pro(Reasoning & Knowledge) | 67 | 71 |
| MATH-500 (Quantitative Reasoning) | 88 | 77 |
| LiveCodeBench (Coding) | 14 | 29 |
| HumanEval(Coding) | 89 | 86 |
| GPQA Diamond(Scientific Reasoning) | 42.4 | 49 |
| MGSM | 74.3 | 91.1 |
| Vision QA (MMMU) | 64.9 | text-only |
Quick takeaways:
- Pure language & coding: Llama 3 wins by a wide margin.
- Vision tasks & OCR: only Gemma 3 supports them.
- Reasoning & knowledge: both are competitive; Llama 3 edges ahead on math and code, Gemma 3 holds its own in multilingual breadth.
If you want to check the ability of Gemma 3 in VL Models, you can see this article: Gemma 3 27B vs Qwen2.5-VL: Best for AI photo Q&A?
Resource Efficiency: Cost and Hardware
This is where the two models diverge most significantly, impacting accessibility and deployment strategy.
1. API pricing (public pay-as-you-go)
| Provider | Gemma 3 27B | Llama 3․3 70B |
|---|---|---|
| Novita AI | $0.119 / M input & $0.20 / M output tokens | $0.13/ M input & $0.39 / M output tokens |
| Deepinfra | $0.09 / M input & $0.17 / M output tokens | $0.23/ M input & $0.40 / M output tokens |
| Parasail | $1.20 / M input & $1.20/ M output tokens | $0.10/ M input & $0.40 / M output tokens |
When assessing API efficiency, you should look beyond just the cost per token—model output speed and response latency are equally crucial for real-world applications.
Or you can directly use the free playground to test the speed from every tasks!
2. Local inference hardware
Llama 3.3 70B:
- VRAM: 24GB (minimum) for 4-bit quantization; 80GB+ (A100/H100) for full precision.
- Recommended: 2x NVIDIA A100/H100 (80GB) .
- RAM: 32–64GB+
- Storage: 250GB+
- Home Setup: Challenging, high power and cooling needs.
Gemma 3 27B:
- VRAM: Fits on 1x H100 (80GB) or 3–4x RTX 4090 (24GB).
- RAM: ~32–64GB
- Storage: 54GB (weights); 72.7GB (with KV cache)
- Home Setup: Easier, more feasible for advanced desktops.
Approx. street pricing (2025Q2):
- RTX 4090 24 GB: ~$1 600
- NVIDIA H100 80 GB: ~$29 000
3. GPU-cloud spot rates
| GPU type | On-demand | Dedicated Endpoints |
|---|---|---|
| A100 80 GB | $1.60/hr | – |
| H100 80 GB | $2.56/hr | $2.41/hr |
| RTX4090 | $1.05/hr(3 cards) | $0.61/hr |
The verdict is clear: Gemma 3 27B lowers the barrier to entry for running a powerful model locally, while Llama 3.3 70B is geared more towards cloud API access or organizations with significant on-premise hardware investment.
Applications: Choosing the Right Tool for the Job
The distinct profiles of these models make them suitable for different applications.
| Use Case | Gemma 3 27B | Llama 3.3 70B |
|---|---|---|
| Chatbots / AI Assistants | Supports 140+ languages, well-suited for global, multilingual conversational AI applications | Excels at instruction following, ideal for demanding English and multilingual assistants |
| Code Generation | Performs well on basic to intermediate code tasks; suitable for prototyping and educational projects | Achieves 88% on HumanEval; strong at complex code generation and debugging for developer tools |
| Long-form Drafting | Handles up to 128k tokens, enabling efficient processing of long documents, reports, or research | Also supports 128k–130k token context for extended drafting and summarization tasks |
| Image Support | Native multimodal input (text + images) with SigLIP encoder, enabling OCR, content moderation, and visual Q&A | No native multimodal capability; limited to text-only inputs |
| On-device / Edge Deployment | 4B and 9B lightweight versions enable efficient local and edge deployment for individuals and SMBs | 8B variant available for edge use; 70B model requires high-end hardware |
How to Access Gemma 3 27B and Llama 3.3 70B via Novita API?
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model
Browse through the available options and select the model that suits your needs.
Step 3: Start Your Free Trial
Begin your free trial to explore the capabilities of the selected model.
Step 4: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 5: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/v3/openai",
api_key="session__FaCoze-7Vk7DBH0noVpc42JxmWIV4gCRV31Rz66AmBkUz5ZglF3sYVyGw3ZPlr08zck6KQHI51Scef6kEm8cQ==",
)
model = "google/gemma-3-27b-it"
stream = True # or False
max_tokens = 16000
system_content = ""Be a helpful assistant""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }
chat_completion_res = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_content,
},
{
"role": "user",
"content": "Hi there!",
}
],
stream=stream,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
response_format=response_format,
extra_body={
"top_k": top_k,
"repetition_penalty": repetition_penalty,
"min_p": min_p
}
)
if stream:
for chunk in chat_completion_res:
print(chunk.choices[0].delta.content or "", end="")
else:
print(chat_completion_res.choices[0].message.content)
The choice between Gemma 3 27B and Llama 3.3 70B is not about which model is “better,” but which is better for you.
Gemma 3 27B represents a leap in AI versatility and efficiency. It brings powerful multimodal capabilities to a more accessible hardware footprint, empowering a new wave of applications that can see and understand the world. It is the perfect tool for innovators who need flexibility and want to run state-of-the-art AI without an enterprise-sized budget.
Llama 3.3 70B is the undisputed champion of pure text-based performance at scale. It offers unparalleled power for reasoning, instruction following, and coding tasks. Combined with its incredibly low API cost, it is the definitive choice for businesses and developers building robust, high-volume applications where linguistic excellence is the primary goal.
Ultimately, your decision will hinge on a simple trade-off: do you need the multimodal versatility and hardware efficiency of Gemma, or the raw text-processing power and API cost-effectiveness of Llama?
Frequently Asked Questions
Yes! Smaller Gemma variants (e.g., 4B) support Apple Silicon via mlx-vlm. The 27B model requires GPU acceleration (e.g., cloud APIs).
Llama 3.3 70B excels in low-latency scenarios . Gemma’s vision processing adds minor overhead.
Yes—it’s free on novita ai playground. However, local deployment demands expensive hardware, while APIs incur token-based costs.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.
Recommend Reading
- Why LLaMA 3.3 70B VRAM Requirements Are a Challenge for Home Servers？
- Qwen 2.5 72b vs Llama 3.3 70b: Which Model Suits Your Needs?
- Is Llama 3.3 70B Really Comparable to Llama 3.1 405B?
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