Memory capacity answers whether the model can load. Bandwidth, compute, context, offload, and concurrency decide whether it feels fast enough.
Use the explanations below when you want to know why each step matters.
Fit and speed are different
Memory capacity asks whether weights, context, and runtime fit. Performance asks how quickly the computer can read those weights, process the prompt, and generate the answer.
A 20 GB model may fit in 32 GB RAM and still feel slow on a CPU. A smaller model that fits fully inside GPU memory can answer much faster. MamiLens should show fit confidence and speed evidence separately.
Weights, context, runtime, and reserve
GPU VRAM is often much faster
Architecture and hardware matter
Long prompts increase delay
Team use changes wait time
Memory bandwidth is the hidden limit
During generation, the computer repeatedly reads model weights. Two computers with the same 32 GB capacity can have very different speeds because their memory systems differ.
Dedicated GPU memory usually provides much higher bandwidth than normal RAM. That is why full VRAM fit often feels much better than splitting a model between VRAM and RAM.
Measure the wait in two parts
Prompt processing happens before the answer starts. A long PDF, code file, or conversation can create a long wait before the first word. Generation speed is what happens after the answer begins.
Record both. A system can generate quickly but feel slow because it spends too long reading the prompt.
Input length, cache, and processing speed
Model size, bandwidth, and compute
The number that matters to the user
Long context uses memory and time
The maximum context on a model card is a limit, not a daily recommendation. The KV cache grows with context. Some architectures use memory more efficiently than others.
A llama.cpp discussion showed a 32 GB system where prompt checkpoints for one model used far more memory than another architecture. A calculator must use architecture and context, not only parameter count.
Use 8K or 16K context first. Increase only when a real task needs more text.
Partial GPU offload is a tradeoff
llama.cpp can place some layers on the GPU and keep the rest in RAM. This is often faster than CPU-only inference, but slower than placing everything in VRAM.
The best number of GPU layers depends on the file, context, GPU, RAM, and backend. Use the engine log and a repeatable benchmark instead of guessing.
Heat changes long tests
Laptops may begin quickly and slow after several minutes when heat builds. A GPU can also run below its best speed when power limits or another program competes for it.
Test after the machine is warm. Record temperature, power mode, and whether the laptop is plugged in. A short cold test can hide the real experience.
A ten-minute troubleshooting order
- Confirm the exact model file and quantization.
- Check the expected CPU or GPU backend.
- Reduce context to 8K and repeat the prompt.
- Close heavy apps and measure again.
- Compare full GPU, partial offload, and CPU-only when possible.
- Measure prompt processing and generation separately.
- Repeat after the machine is warm.
- Test simultaneous users only after one-user speed is acceptable.
How MamiLens should report it
MamiLens can calculate a conservative memory fit from known hardware and artifact size. It should call speed unverified until an exact benchmark exists for the model, quantization, engine, context, and device.
An honest result states likely fit, expected execution path, the reason for the choice, remaining uncertainty, and the exact test to run next.
Official facts and real user evidence
Official documentation supports product and model facts. Community discussions show real setups, failures, and questions. A community result is supporting evidence, not a promise that another computer will perform the same way.
Find a model your computer can run.
MamiLens checks your hardware and shows a careful starting point.
Run the free compatibility check →This guide is educational. Model software, licenses, and hardware support can change. Check official sources before an important deployment.