Check my GPU

on

Can the NVIDIA GeForce RTX 5070 Ti run DeepCoder 14B Preview?

82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 13.5 GB of 16.0 GB usable, with an estimated speed of 61.5 tok/s.

Best quant
Q5_K_M
Generation speed
61.5 tok/s
Prompt processing
2,140 tok/s
First token (1K prompt)
0.5 s
Largest safe context
20K

Download DeepCoder 14B Preview

Recommended for the NVIDIA GeForce RTX 5070 Ti: Q5_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run deepcoder:14b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q5_K_M file (opens in a new tab)

huggingface.co/bartowski/agentica-org_DeepCoder-14B-Preview-GGUF (opens in a new tab)

ollama run hf.co/bartowski/agentica-org_DeepCoder-14B-Preview-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from Agentica, for fine-tuning, vLLM, Transformers.

huggingface.co/agentica-org/DeepCoder-14B-Preview (opens in a new tab)

No sign-in needed.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.27.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total82.9 / 10082 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 5070 Ti

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 8.9 GB 16.0 GB 122 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 20.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 GGUF (opens in a new tab)
Q3_K_M 10.2 GB 16.0 GB 96.3 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 24.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.8/15 GGUF (opens in a new tab)
Q4_K_M 12.0 GB 16.0 GB 74.5 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 13.5 GB 16.0 GB 61.5 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 15.1 GB 16.0 GB 51.7 tok/s Tight fit 82 ptsGreat (A) · 82 of 100 points: Model quality 28.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 18.7 GB 16.0 GB 12.2 tok/s Runs with offload 62 ptsGood (B) · 62 of 100 points: Model quality 28.8/45, Memory fit 10.0/25, Speed 8.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 32.6 GB 16.0 GB 2.2 tok/s Heavy offload 51 ptsUsable (C) · 51 of 100 points: Model quality 28.8/45, Memory fit 4.0/25, Speed 3.5/15, Memory use 15.0/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.