Ollama library

Deepcoder 14B

Ollama library·14B·Transformer·128K ctx·2026-08

Check on my hardwareAdd to compare →Ollama →Hugging Face →

Model size→Hardware requirement→Actual fit

Deepcoder 14B needs ~10.2–18.4 GB of VRAM depending on quantization. The bar is what it needs; the marker is the card's VRAM. Speed figures below are planning estimates.

8 GB

RTX 4060 · 3060 Ti

10.2
8
Q4_K_M · Est. ~3 tok/sSlow · CPU offload

12 GB

RTX 4070 · 3060 12G

10.2
12
Q4_K_M · Est. ~38 tok/sComfortable

16 GB

RTX 4080 · M-series 16G

11.8
16
Q5_K_M · Est. ~49 tok/sComfortable

24 GB

RTX 4090 · 3090

18.4
24
Q8_0 · Est. ~47 tok/sComfortable

48 GB

A6000 · 2×24G

18.4
48
Q8_0 · Est. ~47 tok/sComfortable

02  — Quantizations

QuantFile sizeEst. VRAM
Q4_K_M9 GB10.2 GB
Q5_K_M10.6 GB11.8 GB
Q8_017.2 GB18.4 GB

03  — Runtimes & use

llama.cpp · Ollama · LM Studio

General · Coding

Memory and speed figures are planning estimates, not measured benchmarks. Memory includes a fixed allowance for KV cache and runtime overhead. Long-context prompts push VRAM use above the numbers shown here.