OpenGuardrails Text 2510 — Hardware Requirements & GPU Compatibility
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2.5K downloads
9 likesSpecifications
- Publisher
- openguardrails
- Parameters
- 14.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-02-02
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does OpenGuardrails Text 2510 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 30.2 GB | 36.5 GB | 29.54 GB | Full half-precision — baseline for inference |
Which GPUs Can Run OpenGuardrails Text 2510?
FP16 · 30.2 GBOpenGuardrails Text 2510 (FP16) requires 30.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 40+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 36.5 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run OpenGuardrails Text 2510?
FP16 · 30.2 GB15 devices with unified memory can run OpenGuardrails Text 2510, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does OpenGuardrails Text 2510 need?
OpenGuardrails Text 2510 requires 30.2 GB of VRAM at FP16. Full 41K context adds up to 6.4 GB (36.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 16 bits ÷ 8 = 29.5 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
FP1630.2 GBFP16 + full context36.5 GB- Can I run OpenGuardrails Text 2510 on a Mac?
OpenGuardrails Text 2510 requires at least 30.2 GB at FP16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run OpenGuardrails Text 2510 locally?
Yes — OpenGuardrails Text 2510 can run locally on consumer hardware. At FP16 quantization it needs 30.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OpenGuardrails Text 2510?
At FP16, OpenGuardrails Text 2510 can reach ~97 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 30.2 × 0.55 = ~97 tok/s
Estimated speed at FP16 (30.2 GB)
AMD Instinct MI300X~97 tok/sNVIDIA H100 SXM~72 tok/sAMD Instinct MI250X~60 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OpenGuardrails Text 2510?
At FP16, the download is about 29.54 GB.
- Which GPUs can run OpenGuardrails Text 2510?
1 consumer GPU can run OpenGuardrails Text 2510 at FP16 (30.2 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run OpenGuardrails Text 2510?
15 devices with unified memory can run OpenGuardrails Text 2510 at FP16 (30.2 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.