---
title: "CPU vs GPU Performance in Ollama"  
description: "CPU vs GPU Performance in Ollama"  
author: "Anubhav Sharma"  
published: 2026-07-02  
updated: 2026-07-02  
canonical: https://www.mindstick.com/interview/34525/cpu-vs-gpu-performance-in-ollama  
category: "artificial intelligence"  
tags: ["c#", "artificial intelligence"]  
reading_time: 7 minutes  

---

# CPU vs GPU Performance in Ollama

When running large language models in **Ollama**, the difference between CPU and GPU performance is significant. The best choice depends on your hardware, model size, and intended workload.

## CPU vs GPU Comparison

| Feature | CPU | GPU |
| --- | --- | --- |
| Inference Speed | Slow | Much faster |
| Parallel Processing | Limited | Thousands of cores |
| Memory | Uses system RAM | Uses VRAM |
| Power Efficiency | Lower for AI workloads | Higher for AI workloads |
| Cost | No extra hardware | Requires dedicated GPU |
| Best For | Small models, testing | Large models, production |

## CPU Performance

### Advantages

- No dedicated graphics card required.
- Uses available system RAM.
- Suitable for:

   - Development
   - Small language models
   - Learning Ollama

### Disadvantages

- Much slower token generation.
- High CPU usage.
- Less efficient with models larger than 7B.

Example:

```plaintext
ollama run llama3
```

If no compatible GPU is detected, Ollama automatically runs on the CPU.

Typical speeds:

| Model | CPU Tokens/sec |
| --- | --- |
| 3B | 10–25 |
| 7B | 5–15 |
| 13B | 2–8 |
| 70B | Often impractical |

Performance depends heavily on:

- CPU generation
- Number of cores
- Memory bandwidth
- Quantization level

## GPU Performance

GPUs are designed for massive parallel computation, making them ideal for transformer inference.

### Advantages

- 5×–20× faster inference.
- Lower latency.
- Better for:

   - Coding assistants
   - Chatbots
   - Long context windows
   - Concurrent users

Typical speeds:

| GPU | 7B Model |
| --- | --- |
| RTX 3060 12GB | 45–70 tokens/sec |
| RTX 4070 | 70–120 tokens/sec |
| RTX 4090 | 120–250+ tokens/sec |
| Apple M3 Max | 50–100 tokens/sec |

## VRAM Requirements

Approximate VRAM needed:

| Model | VRAM |
| --- | --- |
| 3B | 2–4 GB |
| 7B | 4–8 GB |
| 13B | 8–12 GB |
| 32B | 20–24 GB |
| 70B | 40–48+ GB |

Quantized models (Q4, Q5, Q6) require less memory.

## CPU Bottlenecks

CPU inference is limited by:

- Matrix multiplication speed
- Cache size
- Memory bandwidth
- Thread scheduling

Even a 16-core CPU cannot match the throughput of a modern GPU for LLM inference.

## GPU Bottlenecks

GPU performance depends on:

- VRAM capacity
- Memory bandwidth
- CUDA cores (NVIDIA)
- Metal Performance Shaders (Apple Silicon)
- ROCm support (AMD)

If the model exceeds available VRAM, Ollama may offload layers to system RAM, reducing performance.

## Mixed CPU + GPU Execution

Ollama can split computation between GPU and CPU when necessary.

Benefits:

- Allows running models larger than available VRAM.

Drawbacks:

- Slower than full GPU execution.
- Increased latency due to data transfer between system RAM and VRAM.

## Example Performance

Prompt:

> "Explain quantum computing in 500 words."

Approximate response times:

| Hardware | Time |
| --- | --- |
| Intel Core i7 (CPU only) | 20–40 seconds |
| RTX 3060 | 4–8 seconds |
| RTX 4070 | 2–5 seconds |
| RTX 4090 | 1–3 seconds |

## Which Should You Choose?

| Use Case | Recommendation |
| --- | --- |
| Learning Ollama | CPU is sufficient |
| Casual chatting | CPU or entry-level GPU |
| Software development | GPU preferred |
| Large code generation | GPU strongly recommended |
| Running 13B+ models | GPU |
| Multi-user API server | High-end GPU |
| Production deployments | GPU |

## Answers

### Answer by Anubhav Sharma

When running large language models in **Ollama**, the difference between CPU and GPU performance is significant. The best choice depends on your hardware, model size, and intended workload.

## CPU vs GPU Comparison

| Feature | CPU | GPU |
| --- | --- | --- |
| Inference Speed | Slow | Much faster |
| Parallel Processing | Limited | Thousands of cores |
| Memory | Uses system RAM | Uses VRAM |
| Power Efficiency | Lower for AI workloads | Higher for AI workloads |
| Cost | No extra hardware | Requires dedicated GPU |
| Best For | Small models, testing | Large models, production |

## CPU Performance

### Advantages

- No dedicated graphics card required.
- Uses available system RAM.
- Suitable for:

   - Development
   - Small language models
   - Learning Ollama

### Disadvantages

- Much slower token generation.
- High CPU usage.
- Less efficient with models larger than 7B.

Example:

```plaintext
ollama run llama3
```

If no compatible GPU is detected, Ollama automatically runs on the CPU.

Typical speeds:

| Model | CPU Tokens/sec |
| --- | --- |
| 3B | 10–25 |
| 7B | 5–15 |
| 13B | 2–8 |
| 70B | Often impractical |

Performance depends heavily on:

- CPU generation
- Number of cores
- Memory bandwidth
- Quantization level

## GPU Performance

GPUs are designed for massive parallel computation, making them ideal for transformer inference.

### Advantages

- 5×–20× faster inference.
- Lower latency.
- Better for:

   - Coding assistants
   - Chatbots
   - Long context windows
   - Concurrent users

Typical speeds:

| GPU | 7B Model |
| --- | --- |
| RTX 3060 12GB | 45–70 tokens/sec |
| RTX 4070 | 70–120 tokens/sec |
| RTX 4090 | 120–250+ tokens/sec |
| Apple M3 Max | 50–100 tokens/sec |

## VRAM Requirements

Approximate VRAM needed:

| Model | VRAM |
| --- | --- |
| 3B | 2–4 GB |
| 7B | 4–8 GB |
| 13B | 8–12 GB |
| 32B | 20–24 GB |
| 70B | 40–48+ GB |

Quantized models (Q4, Q5, Q6) require less memory.

## CPU Bottlenecks

CPU inference is limited by:

- Matrix multiplication speed
- Cache size
- Memory bandwidth
- Thread scheduling

Even a 16-core CPU cannot match the throughput of a modern GPU for LLM inference.

## GPU Bottlenecks

GPU performance depends on:

- VRAM capacity
- Memory bandwidth
- CUDA cores (NVIDIA)
- Metal Performance Shaders (Apple Silicon)
- ROCm support (AMD)

If the model exceeds available VRAM, Ollama may offload layers to system RAM, reducing performance.

## Mixed CPU + GPU Execution

Ollama can split computation between GPU and CPU when necessary.

Benefits:

- Allows running models larger than available VRAM.

Drawbacks:

- Slower than full GPU execution.
- Increased latency due to data transfer between system RAM and VRAM.

## Example Performance

Prompt:

> "Explain quantum computing in 500 words."

Approximate response times:

| Hardware | Time |
| --- | --- |
| Intel Core i7 (CPU only) | 20–40 seconds |
| RTX 3060 | 4–8 seconds |
| RTX 4070 | 2–5 seconds |
| RTX 4090 | 1–3 seconds |

## Which Should You Choose?

| Use Case | Recommendation |
| --- | --- |
| Learning Ollama | CPU is sufficient |
| Casual chatting | CPU or entry-level GPU |
| Software development | GPU preferred |
| Large code generation | GPU strongly recommended |
| Running 13B+ models | GPU |
| Multi-user API server | High-end GPU |
| Production deployments | GPU |


---

Original Source: https://www.mindstick.com/interview/34525/cpu-vs-gpu-performance-in-ollama

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