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VRAMwise

llama.cpp vs vLLM memory behavior

Weights are portable; memory peaks are not.

llama.cpp world

GGUF quants, single-user interactive defaults, CPU offload knobs. VRAMwise tables skew toward this world.

vLLM world

Serving many concurrent requests changes KV layout and peaks. A “fits on 24GB” claim from a blog may assume batch 1.

Rule

Always re-measure with the engine and batch you will ship. Use our formulas as a first filter, not a production capacity plan.

General information, not personalized advice.

Models in band
Llama 3.1 8B Instruct · Llama 3.3 70B Instruct · Qwen3 8B · Qwen3 14B · Qwen3 32B
Apple contrast
M1 · M1 Pro · M1 Max
Runtime tools
VRAM fit · Context VRAM · Quant compare
Catalog exits
Guides · Tools
Methodology paths
Fit checker · Our formula

Frequently asked questions

Same model, different VRAM?

Yes. Runtime, batch size, cache strategy, and precision paths change peaks.

Which is for laptops?

llama.cpp-style stacks dominate consumer local use with GGUF quants.

Which is for servers?

vLLM-style engines target throughput serving with paged attention.

Can one VRAM number cover both?

No. Re-estimate inside the runtime you will run.

Advice?

Educational contrast.

llama.cpp vs vLLM Memory Behavior — by VRAMwise ↗