AI Rig Match

LOCAL AI COMPATIBILITY

AI Rig Match

Match your GPU or Apple silicon Mac with local language, coding, image, and video models.

Capacity and speed are modeled estimates, not a guarantee. Confirm runtime, driver, and model-version requirements before buying hardware.

YOUR RIG

Build a hardware profile

Set the memory budget and workload that matter for this run.

89 hardware presets · 92 models · verified 2026-08-23

MATCH REPORT

Compatible models

Results separate native VRAM fit from slower system-memory offload.

48Native
3Offload
8Upgrade

Qwen3 0.6B

0.6Bllama.cpp

Qwen · Confidence: medium

Native fit
Model memory0.9 / 29.4 GB
Estimated speed
452.9–685.2 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/qwen3-0.6b-q4_K_M.gguf

Llama 3.2 1B

1Bllama.cpp

Llama · Confidence: medium

Native fit
Model memory1.3 / 29.4 GB
Estimated speed
423–640 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/llama-3-2-1b-q4_K_M.gguf

Gemma 3 1B

1Bllama.cpp

Gemma · Confidence: medium

Native fit
Model memory1.3 / 29.4 GB
Estimated speed
423–640 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/gemma-3-1b-q4_K_M.gguf

Qwen3 1.7B

1.7Bllama.cpp

Qwen · Confidence: medium

Native fit
Model memory1.5 / 29.4 GB
Estimated speed
355.1–537.2 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/qwen3-1.7b-q4_K_M.gguf

Llama 3.2 3B

3Bllama.cpp

Llama · Confidence: medium

Native fit
Model memory2.4 / 29.4 GB
Estimated speed
248.2–375.5 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/llama-3-2-3b-q4_K_M.gguf

SmolLM3 3B

3Bllama.cpp

Hugging Face · Confidence: medium

Native fit
Model memory2.4 / 29.4 GB
Estimated speed
256.4–387.8 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/smollm3-3b-q4_K_M.gguf

Ministral 3B

3Bllama.cpp

Mistral · Confidence: medium

Native fit
Model memory2.6 / 29.4 GB
Estimated speed
241–364.5 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/ministral-3b-q4_K_M.gguf

Phi-4 Mini

3.8Bllama.cpp

Microsoft · Confidence: medium

Native fit
Model memory2.9 / 29.4 GB
Estimated speed
221.9–335.8 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/phi-4-mini-q4_K_M.gguf

Phi-3.5 Mini

3.8Bllama.cpp

Microsoft · Confidence: medium

Native fit
Model memory3 / 29.4 GB
Estimated speed
215.6–326.2 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/phi-3.5-mini-q4_K_M.gguf

Qwen3 4B

4Bllama.cpp

Qwen · Confidence: medium

Native fit
Model memory3.3 / 29.4 GB
Estimated speed
200.2–302.9 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/qwen3-4b-q4_K_M.gguf

Gemma 3 4B

4Bllama.cpp

Gemma · Confidence: medium

Native fit
Model memory3.5 / 29.4 GB
Estimated speed
192–290.5 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/gemma-3-4b-q4_K_M.gguf

Gemma 3n E2B

5B / 2B activellama.cpp

Gemma · Confidence: medium

Native fit
Model memory4 / 29.4 GB
Estimated speed
173–261.7 tok/s
Quantization
Q4
Context
8K
Minimum RAM
16 GB
llama-cli -m ./models/gemma-3n-e2b-q4_K_M.gguf

Ranges include runtime overhead and an OS reserve. Real performance varies by model build, backend, driver, thermals, and prompt.

CALCULATION METHOD

A fit result with its assumptions exposed

The estimate treats weights, context, runtime overhead, and memory topology as separate constraints.

Weights before marketing labels

Each profile starts from a quantized weight footprint and adds runtime headroom instead of equating parameter count with VRAM.

Context has a real cost

Longer context adds KV-cache pressure. The same model can move from native fit to offload when context grows.

Offload is not native fit

System-memory offload is shown separately because it can run while delivering a very different speed and latency experience.

COMMON QUESTIONS

Local AI hardware questions

How much VRAM does an 8B language model need?

A Q4 build commonly needs about 5–7 GB after basic runtime overhead. Longer context, a larger batch, or higher precision can push it beyond 8 GB.

Is Apple unified memory the same as GPU VRAM?

No. Apple silicon shares one memory pool between the CPU and GPU. That flexibility helps larger models fit, but macOS and other processes still need a reserve.

What does offload mean?

Part of the model stays in system RAM and moves through the CPU or PCIe path. It can make a model launch, but it is usually much slower than keeping all active data in GPU memory.

Are the speed ranges benchmark results?

They are conservative modeled ranges, not a benchmark claim. Exact speed depends on runtime, kernel support, model build, driver, cooling, and workload.