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Model Size vs Inference Speed Tradeoffs

13 posts on model size vs inference speed tradeoffs — part of benchmarks & performance on the n4n AI blog.

13 postsBenchmarks & performanceAll posts
Benchmarks & performanceAnalysis

Why a 7B model can outrun a 34B model on tokens per second

A 7B model often delivers higher tokens per second than a 34B model due to memory bandwidth, batching, and quantization. Here's the engineering tradeoff.

5 min read
Benchmarks & performanceAnalysis

Qwen2.5 0.5B to 72B: mapping the speed-to-size curve

An engineering analysis of the Qwen2.5 model size speed curve from 0.5B to 72B, covering throughput, latency, quantization, and where the tradeoffs make sense.

4 min read
Benchmarks & performanceComparison

Quantized 70B vs native 13B: which wins on speed?

A practical head-to-head of quantized 70B vs native 13B model speed: latency, cost, quality, and ergonomics to help you pick the right size for your LLM workload.

4 min read
Benchmarks & performanceComparison

Phi-3 mini vs Llama 3 8B: punching above its weight class

Practical head-to-head comparison of Phi-3 mini vs Llama 3 8B speed, inference cost, and real-world capabilities for engineers, with a use-case-based deployment verdict.

4 min read
Benchmarks & performanceAnalysis

Parameter count vs real-world latency: what actually matters

Parameter count vs real-world latency: why model size alone misleads engineers, and which architectural and serving factors actually dictate inference speed.

6 min read
Benchmarks & performanceAnalysis

Mixture-of-experts models and the size vs speed myth

Mixture of experts inference speed debunks the bigger-is-slower myth. We analyze MoE tradeoffs in parameter count, memory bandwidth, and latency.

4 min read
Benchmarks & performanceComparison

Mixtral 8x7B vs a 13B dense model: which is faster?

A head-to-head engineering comparison of Mixtral 8x7B and 13B dense models: latency, throughput, cost, and which to use for real workloads.

5 min read
Benchmarks & performanceAnalysis

How active parameters change speed math for MoE models

Active parameters, not total weights, dictate MoE inference speed. We break down the math, overhead, and tradeoffs for engineering capacity plans.

5 min read
Benchmarks & performanceAnalysis

Does model size still predict inference latency in 2026?

Model size inference latency 2026 is no longer a simple correlation; serving stack, quantization, and routing now dominate LLM response times.

4 min read
Benchmarks & performanceAnalysis

Small models, big throughput: the case for sub-10B LLMs

Sub-10B LLMs deliver order-of-magnitude throughput gains for most production tasks. This analysis weighs tradeoffs and gives a routing framework for engineers.

4 min read
Benchmarks & performanceComparison

Llama 3.1 8B vs 70B: speed vs quality tradeoffs

Practical comparison of Llama 3.1 8B vs 70B speed, cost, and quality tradeoffs for engineers shipping LLM apps, with latency and use-case guidance.

4 min read
Benchmarks & performanceComparison

GPT-4o mini vs GPT-4o: latency gap by the numbers

Benchmark-backed head-to-head of gpt-4o mini vs gpt-4o latency, throughput, cost, and capabilities for engineers shipping LLM apps.

5 min read
Benchmarks & performanceComparison

Claude Haiku vs Claude Sonnet: latency per model tier

Claude Haiku vs Sonnet latency per model tier: engineering comparison of cost, capability, and routing tradeoffs for production LLM systems.

5 min read

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