Model compatibility tool

Can I Run This AI Model?

A model is runnable only when its weights plus runtime overhead, context/KV cache and concurrent-request memory fit the memory available to the inference runtime. Use the checker for a transparent planning screen, then validate the exact deployment.

Check a model against your hardware

Choose a verified artifact and memory configuration. The checker adds explicit headroom instead of treating download size as runtime requirement.

Result

Likely fit

Planning requirement

6.5 GB

Headroom after estimate

9.5 GB

Important: planning screen, not benchmark. Runtime, context/KV cache, GPU offload and concurrency can change memory use. Artifact: 5.2 GB; context: 40K. Evidence checked 2026-09-16.

Permanent result link →

Embed this compatibility checker

Use the live checker on your site free with attribution to the evidence and methodology.

Share & cite the result

Add an AIG compatibility badge

Use the badge in a GitHub README, model card, hardware guide or website. It links readers to the exact evidence-backed compatibility result.

Selected AIG model compatibility badge
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[![AIG compatibility](https://agentinfraguide.com/api/v1/badges/compatibility-pair/qwen3-8b/mac-m6-16)](https://agentinfraguide.com/tools/can-i-run-this-model?model=qwen3-8b&hardware=mac-m6-16&reserve=25&utm_source=badge&utm_medium=referral&utm_campaign=compatibility_badge)
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<a href="https://agentinfraguide.com/tools/can-i-run-this-model?model=qwen3-8b&hardware=mac-m6-16&reserve=25&utm_source=badge&utm_medium=referral&utm_campaign=compatibility_badge" rel="noopener"><img src="https://agentinfraguide.com/api/v1/badges/compatibility-pair/qwen3-8b/mac-m6-16" alt="AI model hardware compatibility — Agent Infra Guide" loading="lazy"></a>
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https://agentinfraguide.com/api/v1/badges/compatibility-pair/qwen3-8b/mac-m6-16
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https://agentinfraguide.com/tools/can-i-run-this-model?model=qwen3-8b&hardware=mac-m6-16&reserve=25&utm_source=badge&utm_medium=referral&utm_campaign=compatibility_badge

Badge links include referral attribution so AIG can measure which embeds send useful traffic. The badge itself contains no tracking script.

How the checker works

What Determines Whether an AI Model Fits?

Parameter count alone is not enough. Quantization changes weight memory, while context, caches, runtime allocations and concurrency add memory beyond the model artifact.

Why quantization matters

Hugging Face documents 8-bit inference as roughly halving weight memory versus full precision, while 4-bit methods compress weights further. The checker uses current Ollama artifact sizes rather than inferring a file size from parameter count.

Why the reserve is explicit

The exact runtime requirement depends on architecture, context length, KV-cache representation, runtime, GPU offload and concurrency. A visible reserve makes the assumption inspectable instead of hiding false precision inside a formula.

What comes next

AIG will expand this dataset with exact quantizations, measured runtime memory, throughput, concurrency and AIG-tested configurations. Those measured records will replace planning estimates wherever evidence exists.

Next steps

Estimate RAM / VRAM →Qwen hardware requirements →Qwen2.5-Coder hardware →Llama 3.2 hardware →Phi-4 hardware →Mac mini for local AI →

Sources

Agent Infrastructure Recommendation Kit

Turn model compatibility into a complete infrastructure decision

The Agent Infrastructure Recommendation Kit gives you a six-step system to define requirements, estimate capacity, compare local, cloud, VPS, GPU, and hybrid options, document evidence, and make the recommendation.

Get the Recommendation Kit — $19