GGUF Q4_K_M Model Size Calculator
File size and RAM needed for any model in llama.cpp's most popular quant — Q4_K_M.
Q4_K_M is the community default for llama.cpp: k-quant blocks store 4-bit weights with 6-bit scales, and the most sensitive tensors (attention.wv, ffn_down in part) are kept at Q6_K — hence ~4.84 effective bits per weight, not a flat 4.0.
Formula
About GGUF Q4_K_M Model Size Calculator
Q4_K_M is the community default for llama.cpp: k-quant blocks store 4-bit weights with 6-bit scales, and the most sensitive tensors (attention.wv, ffn_down in part) are kept at Q6_K — hence ~4.84 effective bits per weight, not a flat 4.0. This calculator turns any parameter count into a concrete file size and a realistic total-memory figure, so you can check whether a given checkpoint fits your GPU VRAM or system RAM before downloading tens of gigabytes. It uses the measured effective bits-per-weight of the format — including block scales and mixed-precision tensor exceptions — rather than the marketing bit-width.
How to use GGUF Q4_K_M Model Size Calculator
- 1Enter your values into GGUF Q4_K_M Model Size Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use GGUF Q4_K_M Model Size Calculator?
- ✓Computes GGUF Q4_K_M Model Size instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: size(GB) = params × 4.84 bits ÷ 8 ÷ 10⁹ (4.84 = measured effective bits/weight for this format, incl. scales).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why is Q4_K_M bigger than parameters × 4 bits?+
K-quants store per-block scale and min values (super-blocks of 256 weights), and Q4_K_M deliberately keeps about 10% of the most quality-critical tensors at Q6_K. Measured across models, the effective rate lands near 4.84 bits per weight.
How much quality does Q4_K_M lose?+
Typically +0.05–0.1 perplexity on 7B-class models versus FP16 — usually imperceptible in chat. It is widely considered the best size/quality trade-off in the GGUF family, which is why most Hugging Face GGUF repos mark it 'recommended'.
Does the GGUF file size equal RAM usage?+
Almost — GGUF is memory-mapped, so resident memory ≈ file size, plus the KV cache and a small compute buffer. Add roughly 0.5–2 GB depending on context length, which this estimate's overhead output approximates.
Can I run Q4_K_M on CPU only?+
Yes; that is llama.cpp's home turf. A 7B Q4_K_M (~4.1 GB) runs on any 8 GB-RAM machine at several tokens/s on a modern laptop CPU, and you can split layers between CPU and GPU with the -ngl flag for speed.
Related tools
- GGUF Q8_0 Model Size Calculator
- GGUF Q2_K Model Size Calculator
- GPTQ 4-bit Model Size Calculator
- AWQ 4-bit Model Size Calculator
- FP8 Model Size Calculator
- Transformer Parameter Count Calculator
- Classification Metrics — Confusion Matrix & Metrics Calculator
- Spam Filter — Confusion Matrix & Metrics Calculator
Related ML & AI tools
ROC-AUC Calculator (from TPR/FPR points)
Trapezoidal area under the ROC curve from your (FPR, TPR) operating points — the threshold-independent ranking score.
● LiveClassification Threshold Cost Calculator
Find the probability cutoff that minimizes expected cost given your false-positive and false-negative penalties.
● LiveSilhouette Score Calculator
Cluster cohesion vs separation for one point — the building block of the silhouette metric for choosing K.
● Live