AI Models

Open models to run on your own machine

53 open-source models selected for local deployment. Each with its use case, the memory it needs and how to install it.

Source: Hugging Face and Ollama. Sizes checked on 6 October 2026.

53 models 8 categories 20 machines compared

Which model fits your machine?

Pick a model and a quantisation to see which machines it fits.

Work out the memory a model needs

Pick a model, a quantisation and a context. The bars show which machines it fits.

Category
I have this hardware
Quantisation
Context (tokens the model keeps in memory)

Popular models

Total memory–
Parameters–
Max context–
License–
Good for–

Estimated memory needed

–weights + –context cache + 1.5 GBruntime = –total

Weights = parameters × bits per weight ÷ 8 (Q4_0 4.5 · Q4_K_M 4.85 · Q8_0 8.5 · FP16 16). Cache = MB per token × context tokens. Runtime = 1.5 GB.

Measured with ollama ps on our GB10, real use comes in 1.4 to 1.6 GB below this figure: we would rather you have memory to spare than run short. From each machine we also take off the share the operating system keeps.

Estimated speed: –

Which machines it fits

From least to most memory. Open a machine for its spec sheet and where to buy it.

Cloud GPU rental (hourly)

  • Cloud A100 80GB (RunPod) 80 GB · hourly rental
  • Cloud H100 80GB (RunPod) 80 GB · hourly rental
Fits with room Tight (over 85% of usable) Does not fit

Tip:

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Next step: Hardware selector ROI calculator Grants

Local model catalogue

My hardware:
More filters: provider

53 models

Click a model for details

Hermes 3 8B

Specialized

Nous Research · 8B · 131.072K ctx · Llama 3.1 Community

Best community fine-tune for agentic use. Excellent at following complex system prompts and using tools.

Agentic tasksTool useSystem prompts ~42 tok/s
Q4_K_M
4.92 GB ≤ 8 GB
Q8
8.54 GB ≤ 16 GB
FP16
16.1 GB ≤ 24 GB
Model details
Recommended hardware

Jetson Orin Nano 8GB (Q4) or Mac Mini M4

Indicative speed (M3 Pro 32 GB, not measured by us)
Q4: 42 tok/s FP16: 20 tok/s
Install with Ollama
ollama pull hermes3:8b
NousResearch/Hermes-3-Llama-3.1-8B

Finance-LLM 13B

Specialized

TheBloke (quantized) · 13B · 4.096K ctx · Llama 2 Community

Fine-tuned on financial data. Understands financial terminology, ratios, and market concepts.

Financial analysisReport generationMarket research ~22 tok/s
Q4_K_M
7.87 GB ≤ 8 GB
Q8
13.8 GB ≤ 16 GB
FP16
26 GB > 24 GB
Model details
Recommended hardware

Mac Mini M4 24GB (Q8) or RTX 4060 Ti (Q4)

Indicative speed (M3 Pro 32 GB, not measured by us)
Q4: 22 tok/s Financial domain
Install with Ollama
ollama pull finance-llm:13b
TheBloke/finance-LLM-13B-GGUF

Dolphin 2.9 8B

Specialized

Cognitive Computations · 8B · 8.192K ctx · Llama 3 Community

Uncensored fine-tune, no alignment filtering. Useful for creative tasks, red-teaming, research.

Uncensored assistantCreative writingRoleplay ~42 tok/s
Q4_K_M
4.92 GB ≤ 8 GB
Q8
8.54 GB ≤ 16 GB
FP16
16.1 GB ≤ 24 GB
Model details
Recommended hardware

Jetson Orin Nano 8GB (Q4) or any device

Indicative speed (M3 Pro 32 GB, not measured by us)
Q4: 42 tok/s FP16: 20 tok/s
Install with Ollama
ollama pull dolphin-llama3:8b
dphn/dolphin-2.9-llama3-8b

Llama Guard 3

Specialized

Meta · 8B · 131.072K ctx · Llama 3.1 Community

Meta's safety classifier. Run alongside any model to filter harmful content. Essential for production deployments.

Content moderationSafety classificationGuardrails ~40 tok/s
Q4_K_M
4.92 GB ≤ 8 GB
Q8
8.54 GB ≤ 16 GB
FP16
16.1 GB ≤ 24 GB
Model details
Recommended hardware

Jetson Orin Nano 8GB (Q4) or any device

Indicative speed (M3 Pro 32 GB, not measured by us)
Q4: 40 tok/s Safety classifier
Install with Ollama
ollama pull llama-guard3:8b
meta-llama/Llama-Guard-3-8B

OCRonos Vintage

Specialized

PleIAs · 124M · 8.192K ctx · Apache 2.0

Specialized in correcting OCR errors from historical documents. Perfect for digitization projects. Apache 2.0.

Historical OCR correctionDocument digitizationArchive processing
Q4_K_M
82 MB ≤ 8 GB
Q8
133 MB ≤ 8 GB
FP16
256 MB ≤ 8 GB
Model details
Recommended hardware

Any device (124M parameters)

Indicative speed (M3 Pro 32 GB, not measured by us)
~5 pages/min (M3 Pro): OCR model
Install with Ollama
ollama pull ocronos
PleIAs/OCRonos-Vintage

Tool

AI Model Comparer

Select 2-3 models for a side-by-side comparison.

Reference: the cloud

When you need the cloud

For jobs that need a bigger model than your machine holds, this is what the API costs. We only publish prices we have checked on each provider's official page.

US dollars per million tokens. Verified on 9 September 2026.
Model Provider Input Output Context
GPT-5 OpenAI $1.25 $10.00 400K
GPT-5 Mini OpenAI $0.25 $2.00 400K
Claude Sonnet 5 Anthropic $2.00 $10.00 200K
Claude Haiku 4.5 Anthropic $1.00 $5.00 200K

Sources: OpenAI · Anthropic. To compare with what it costs locally, use the cost calculator.

Cloud or local?

Cloud API

  • Access to the largest models
  • No hardware to maintain
  • Instant scaling
  • Data leaves your network
  • Cost grows with use
  • Provider dependency

Local AI

  • Data never leaves your network
  • Fixed cost (hardware once)
  • GDPR easier to meet
  • No network latency
  • Smaller models
  • Initial investment
Consult local deployment →
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