CPU vs GPU Performance in Ollama

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Anubhav Sharma · Answer

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: DevelopmentSmall language modelsLearning Ollama Disadvantages Much slower token generation.High CPU usage.Less efficient with models larger than 7B. Example: 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 generationNumber of coresMemory bandwidthQuantization level GPU Performance GPUs are designed for massive parallel computation, making them ideal for transformer inference. Advantages 5&#215;–20&#215; faster inference.Lower latency.Better for: Coding assistantsChatbotsLong context windowsConcurrent 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 speedCache sizeMemory bandwidthThread scheduling Even a 16-core CPU cannot match the throughput of a modern GPU for LLM inference. GPU Bottlenecks GPU performance depends on: VRAM capacityMemory bandwidthCUDA 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

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

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

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

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

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CPU vs GPU Performance in Ollama

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CPU vs GPU Comparison

CPU Performance

Advantages

Disadvantages

GPU Performance

Advantages

VRAM Requirements

CPU Bottlenecks

GPU Bottlenecks

Mixed CPU + GPU Execution

Example Performance

Which Should You Choose?