NC LOCAL AI / MEMORY FIT

Can Your PC Run a Local AI Model?

Pick a model, quantization, context length and graphics card to see if it fits in memory.

Selection models released 2 Jun 2026 to 2 Oct 2026 (dated cohort) Newest model carried 22 Sep 2026 Baseline sources checked 15 Sep 2026 Latest source check 3 Nov 2026

Models: recent releases, plus the most downloaded on Hugging Face. Last verified 3 Nov 2026. Check dates are in the tables below.

Covers releases up to 2 Oct 2026. Newer ones are added after a source check.

16 MODELS / 5 PROFILES
Generic sizes. Vendor formats (NVFP4, FP8, INT4) are approximate.
INITIAL ESTIMATE

Gemma 4 12B on 8 GiB graphics card

Insufficient memory for this configuration
Estimated weights6.1 GiB
Context reserve0.4 GiB
Runtime overhead1.5 GiB
Estimated requirement8.0 GiB
Usable target memory7.2 GiB

Estimated memory against usable target

7.2 GiB usable of 8 GiB
Estimated weights 6.1 GiB
Context reserve 0.4 GiB
Runtime overhead 1.5 GiB
Total estimated requirement 8.0 GiB

Google describes this as a local multimodal model and publishes downloadable checkpoints.

Estimate only: memory fit is not a speed, output-quality, runtime-support, or compatibility guarantee.

HOW IT WORKS

Memory needed = weights + context + overhead

How it's calculated

  • Weights: parameters × bits ÷ 8, plus 10%.
  • Context: grows with the context length you pick.
  • Overhead: a set amount per model for the runtime.
  • Reserve: 10% of your memory stays free.

Based on Ollama's GPU docs and llama.cpp's quantization notes.

Not a guarantee

  • A pass means it should fit in memory.
  • It may still fail to load.
  • It says nothing about speed.
  • Long contexts and some runtimes need more.

Before you download

  1. Set the quantization and context you will really use.
  2. Check the model card.
  3. Check your runtime supports the format.

Q4-Q8 and FP16 are generic sizes. Some models ship their own formats (for example NVFP4, MXFP4, INT4 or FP8), so their estimate is approximate.

Hosted models instead? Compare provider plans. Which runtimes are active? Open-source AI tracker.

RELEASES 2 Jun 2026 TO 2 Oct 2026 / MOST DOWNLOADED
Release window
ModelReleasedWeightsChecked
Gemma 4 12B 3 Jun 2026 2 Nov 2026
NVIDIA Nemotron 3 Ultra 4 Jun 2026 2 Nov 2026
NVIDIA Nemotron 3.5 Lightning 30B-A3B 11 Aug 2026 NVFP4 2 Nov 2026
NVIDIA Nemotron 3.5 Content Safety 2 Jun 2026 BF16 2 Nov 2026
NVIDIA Nemotron 3.5 ASR Streaming 0.6B 4 Jun 2026 BF16 2 Nov 2026
DeepSeek-V4-Flash-0731 31 Jul 2026 FP8 (FP4 cache options) 2 Nov 2026
Kimi K3 16 Jul 2026 MXFP4 weights / MXFP8 activations (QAT) 15 Sep 2026
Kimi K2.7 Code 16 Jul 2026 Native INT4 2 Nov 2026
Qwen3 12B, E-AI compressed V1.5 1 Jul 2026 2 Nov 2026
Qwen3.8-27B release note 14 Aug 2026 BF16 2 Nov 2026
MiMo-V2.6-Distill-Qwen-9B release note 22 Sep 2026 BF16 2 Nov 2026
Most downloaded
ModelReleasedWeightsChecked
Qwen3-14B release note 29 Apr 2025 BF16 3 Nov 2026
Gemma 4 31B IT release note 2 Apr 2026 BF16 3 Nov 2026
gpt-oss-20b release note 5 Aug 2025 MXFP4 (MoE weights), BF16 other layers 3 Nov 2026
Llama 3.1 8B Instruct release note 23 Jul 2024 BF16 3 Nov 2026
NVIDIA Nemotron 3 Nano 4B release note 16 Mar 2026 BF16 3 Nov 2026
Hardware profiles
MemoryExample cardsChecked
8 GiB VRAM GeForce RTX 4060 2 Nov 2026
12 GiB VRAM GeForce RTX 4070 SUPER 2 Nov 2026
16 GiB VRAM GeForce RTX 4080 SUPER
Radeon RX 9070 XT
GeForce RTX 4060 Ti 16GB
2 Nov 2026
24 GiB VRAM GeForce RTX 4090
Radeon RX 7900 XTX
2 Nov 2026
32 GiB VRAM GeForce RTX 5090 2 Nov 2026