Context Length Calculator

Estimate LLM performance and memory requirements at different context lengths

Select Model

Max Context

33K

Base RAM

24 GB

Select Hardware

Available RAM

128 GB

Target Context Length

100.0K

tokens context

Estimated Performance

Prompt Speed

243

tokens/sec

Generation Speed

tokens/sec

KV Cache

3500.00

Total RAM

3524.0

❌

Not enough RAM on Framework Desktop (Ryzen AI Max)

Need 3396 GB more RAM

Time to process 500 tokens

2.06s

Time to generate 100 tokens

4.17s

Context vs Performance

300t/s

10K

286t/s

20K

272t/s

50K

255t/s

100K

243t/s

200K

231t/s

Real Benchmarks: Ryzen AI Max 395+ (128GB)

Based on actual measurements from r/LocalLLaMA

Qwen 3.5-35B @ 100K

246 t/s

Qwen 3.5-35B @ 250K

134 t/s

Qwen 3.5-122B @ 100K

122 t/s

Qwen 3.5-122B @ 250K

63 t/s

Tips for Long Context

💾

KV Cache is Key

Longer context = more KV cache memory

⚡

Use Flash Attention

Reduces memory and improves speed

🎯

Right-size Context

Don't use 128K if you only need 16K

🔧

Try llama.cpp

Optimized for long context inference

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How to Use Context Length Calculator

Figure out what hardware you need to run your model locally.

1Select an open-weights model (e.g., Llama 3 70B, Qwen 2.5)
2Select your target context length (e.g., 128k)
3Input your hardware (Mac M-series or Nvidia GPUs)
4Check KV Cache requirements and generation speeds

Who Is Context Length Calculator For?

For local AI enthusiasts and enterprise hardware planners.

Local LLM Enthusiasts

Plan hardware upgrades

MLOps

Provision the right cloud instances for RAG pipelines

Frequently Asked Questions

What is KV Cache?▼

As context increases linearly, memory requirements scale rapidly. KV Cache is the memory needed to store the attention key/value tensors.