LLM

https://collabnix.com/best-ollama-models-in-2025-complete-performance-comparison/

For Production Deployment:

Primary Choice: DeepSeek-R1 32B for reasoning-heavy applications

Coding Tasks: Qwen2.5-Coder 7B for optimal balance of capability and efficiency

General Purpose: Llama 3.3 70B for maximum versatility

Edge Computing: Phi-4 14B for resource-constrained environments

Optimization Strategies:

Always enable Flash Attention and KV-cache quantization

Use Q4_K_M quantization for production deployments

Implement caching for repeated queries

Monitor GPU memory usage and implement automatic model swapping

Use load balancing for high-throughput applications

Hardware	Llama 3.3 8B (tokens/sec)	Llama 3.3 70B (tokens/sec)	Llama 3.2
RTX 4090	89.2	12.1
RTX 3090	67.4	8.3
A100 40GB	156.7	45.2
M3 Max 128GB	34.8	4.2
Strix Halo 128GB ollama	5.1	85.02
Strix Halo 128GB llama.cpp	90
RTX 3060	131.76

ROCM

model	capabilities	size	context	quantization	eval rate [token/s]	prompt eval rate [token/s]
qwen3-coder	completion tools	“30.5B”	262144	“Q4KM”	52.10	776.55
llama3.2	completion tools	“3.2B”	131072	“Q4_K_M”	52.78	1957.30
gpt-oss:20b	completion tools thinking	“20.9B”	131072	“MXFP4”	45.37	519.90

VULKAN

model	capabilities	size	context	quantization	eval rate [token/s]	prompt eval rate [token/s]
qwen3-coder	completion tools	“30.5B”	262144	“Q4KM”	54.03	805.43
llama3.2	completion tools	“3.2B”	131072	“Q4_K_M”	52.54	1838.82
gpt-oss:20b	completion tools thinking	“20.9B”	131072	“MXFP4”	43.36	475.60