What changes when you quantize model weights
Quantization represents model weights with fewer bits. It reduces storage and memory use, with a possible change in answer quality. A quantized model still needs memory for its context, inference buffers and runtime. Download size alone cannot tell you whether a deployment will fit your GPU. Hugging Face explains the precision and accuracy tradeoff.
Compare variants of the same base model before changing model families. If you switch both the model and its precision, you cannot tell which change caused a different answer. Record the original repository, exact variant, prompt template and inference settings alongside your evaluation results.
Q4, Q8, FP16 and BF16 refer to different choices
Scroll horizontally to see every column.
| Format | What the label describes | What to check |
|---|---|---|
| Q4_K_M | A GGUF quantization recipe using roughly four-bit weights, with mixed tensor precision | Exact artifact size and quality on your prompts |
| Q8_0 | An eight-bit GGUF weight quantization format | Whether the larger variant leaves enough context memory |
| FP16 / F16 | 16-bit floating-point weights | Hardware support and total runtime memory |
| BF16 | A different 16-bit floating-point representation | Model availability and runtime support |
FP16 and BF16 use the same number of bits per weight but have different numerical ranges and precision. Q8_0 is not an FP8 format. Likewise, a GGUF file is a container, not a guarantee that every tensor has four-bit precision. The llama.cpp quantization reference describes recipes, mixed tensors and quantization options.
Use the full suffix when sharing a deployment configuration. Saying only ‘Q4’ hides differences between recipes and publishers. In gpuOS, choose the exact catalog variant, then verify the deployed model rather than assuming an upstream model name identifies one unique artifact.
Calculate weight bytes, then add the rest of memory
The basic arithmetic is parameter count multiplied by bits per weight, divided by eight. For a hypothetical model with exactly eight billion parameters, the following values describe idealized weight storage in decimal GB. They exclude quantization metadata, higher-precision tensors, KV cache and runtime allocations. They are calculation examples, not measured VRAM requirements for an 8B catalog model.
Scroll horizontally to see every column.
| Ideal precision | Calculation | Weight bytes only |
|---|---|---|
| 4 bits | 8,000,000,000 × 4 ÷ 8 | 4 GB |
| 8 bits | 8,000,000,000 × 8 ÷ 8 | 8 GB |
| 16 bits | 8,000,000,000 × 16 ÷ 8 | 16 GB |
Keep units consistent when comparing a file listing with a memory monitor: one decimal GB is one billion bytes, while one GiB is 1,073,741,824 bytes. The gpuOS VRAM calculator uses catalog estimates to help shortlist variants. Confirm the result under your intended workload before buying hardware or assigning traffic.
Evaluate the errors that matter to your application
Build a small evaluation set before choosing the smallest download. Include ordinary requests and the cases that cause expensive mistakes: extracting a number from a table, keeping a required JSON field, citing the right passage, following a language instruction or selecting the right tool. Write expected outcomes so another teammate can repeat the check.
- Compare the same prompts on two precisions of the same base model, keeping context and generation settings fixed.
- Score task success separately from writing style. A fluent answer can contain the wrong identifier or omit a required condition.
- Include long documents and multilingual inputs if they appear in production, rather than evaluating only short English chat.
- Inspect failures directly. Record which inputs fail and whether a larger variant actually fixes them.
Choose acceptance criteria around the product: valid fields, correct citations, useful code or accurate classification. A model that passes your checks at Q4 can be a sensible deployment; one that fails a required behavior needs a different variant, model or application design. No quantization label guarantees task accuracy.
Leave room for context and concurrent requests
A precision change is useful only when the complete workload fits. Test the maximum prompt size you intend to accept, the output allowance and simultaneous requests. Do not fill the GPU with weights and treat the remaining memory as an unlimited chat history budget. The context and KV cache guide explains the separate capacity check.
gpuOS runs Ollama on the GPU machine you connect. On that machine, inspect ollama ps and your GPU memory monitor while requests run. A catalog estimate is a planning input; loaded memory, CPU offloading and latency are deployment observations. Save both in your notes so teammates can reproduce the decision.
Make the final choice with a repeatable comparison
- Shortlist two available variants that fit your estimated hardware budget.
- Run the task evaluation, including the longest representative inputs.
- Measure warm latency and output throughput using the local LLM benchmark method.
- Test the expected request concurrency and keep the results, exact artifact and runtime version together.
- Deploy the variant that meets your quality and service targets with memory headroom, then repeat after material model or runtime changes.