L
localai.computer
ModelsGPUsSystemsAI SetupsBuildsOpenClawMethodology

Resources

  • Methodology
  • Submit Benchmark
  • About

Browse

  • AI Models
  • GPUs
  • PC Builds

Guides

  • OpenClaw Guide
  • How-To Guides

Legal

  • Privacy
  • Terms
  • Contact

© 2025 localai.computer. Hardware recommendations for running AI models locally.

ℹ️We earn from qualifying purchases through affiliate links at no extra cost to you. This supports our free content and research.

Can NVIDIA RTX 6000 Ada run unsloth/Llama-3.2-3B-Instruct?

Runs Q448GB VRAM availableRequires 2GB+

NVIDIA RTX 6000 Ada meets the minimum VRAM requirement for Q4 inference of unsloth/Llama-3.2-3B-Instruct. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.

What this means for you

NVIDIA RTX 6000 Ada can run unsloth/Llama-3.2-3B-Instruct with Q4 quantization. At approximately 232 tokens/second, you can expect Excellent speed - conversational response times under 1 second.

You have 46GB headroom, which is sufficient for system overhead and smooth operation.

Quantization breakdown

QuantizationVRAM neededVRAM availableEstimated speedVerdict
Q42GB48GB232.31 tok/s✅ Fits comfortably
Q83GB48GB150.24 tok/s✅ Fits comfortably
FP166GB48GB80.02 tok/s✅ Fits comfortably

Best current price

NVIDIA RTX 6000 Ada
$7,199.00 on Amazon
Check Price

Suitable alternatives

NVIDIA H200 SXM 141GB
141GB
907.20 tok/s
Price: —
AMD Instinct MI300X
192GB
855.71 tok/s
Price: —
NVIDIA H100 SXM5 80GB
80GB
608.87 tok/s
Price: —
AMD Instinct MI300X
192GB
590.06 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
579.84 tok/s
Price: —

More questions

NVIDIA RTX 6000 Ada specs & pricingFull guide for unsloth/Llama-3.2-3B-Instructunsloth/Llama-3.2-3B-Instruct speed on NVIDIA RTX 6000 Adaunsloth/Llama-3.2-3B-Instruct Q4 requirements