OpenGuardrails Text 2510 — Hardware Requirements & GPU Compatibility
Chat
OpenGuardrails Text 2510 is a 14.8B-parameter open language model from openguardrails. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 9.50 GB of VRAM — see which GPUs and Macs can run it below.
2.5K downloads
9 likesSpecifications
- Publisher
- openguardrails
- Parameters
- 14.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-10-23
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OpenGuardrails Text 2510 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 6.9 GB | 13.3 GB | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 7.8 GB | 14.2 GB | 7.20 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 9.5 GB | 15.9 GB | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 11.2 GB | 17.5 GB | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 12.8 GB | 19.2 GB | 12.18 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 15.4 GB | 21.8 GB | 14.77 GB | 8-bit quantization, near-lossless |
| FP16est. | 16.00 | 30.2 GB | 36.5 GB | 29.54 GB | Full half-precision — baseline for inference |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run OpenGuardrails Text 2510?
Q4_K_M · 9.5 GBOpenGuardrails Text 2510 (Q4_K_M) requires 9.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 15.9 GB. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run OpenGuardrails Text 2510?
Q4_K_M · 9.5 GB49 devices with unified memory can run OpenGuardrails Text 2510, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 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 9.5 GB of VRAM at Q4_K_M, or 30.2 GB at FP16. Full 41K context adds up to 6.4 GB (15.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M9.5 GBQ4_K_M + full context15.9 GB- Can NVIDIA GeForce RTX 4090 run OpenGuardrails Text 2510?
Yes, at Q8_0 (15.4 GB) or lower. Higher quantizations like FP16 (30.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for OpenGuardrails Text 2510?
For OpenGuardrails Text 2510, Q4_K_M (9.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (11.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.9 GB.
VRAM requirement by quantization
Q2_K6.9 GBQ4_K_M ★9.5 GBQ5_K_M11.2 GBQ6_K12.8 GBQ8_015.4 GBFP1630.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OpenGuardrails Text 2510 on a Mac?
OpenGuardrails Text 2510 requires at least 6.9 GB at Q2_K, 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 Q4_K_M quantization it needs 9.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OpenGuardrails Text 2510?
At Q4_K_M, OpenGuardrails Text 2510 can reach ~463 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~69 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 9.5 × 0.65 = ~547 tok/s
Estimated speed at Q4_K_M (9.5 GB)
~547 tok/s~69 tok/s~547 tok/s~463 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 Q4_K_M, the download is about 8.86 GB. The full-precision FP16 version is 29.54 GB. The smallest option (Q2_K) is 6.28 GB.
- Which GPUs can run OpenGuardrails Text 2510?
39 consumer GPUs can run OpenGuardrails Text 2510 at Q4_K_M (9.5 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OpenGuardrails Text 2510?
52 devices with unified memory can run OpenGuardrails Text 2510 at Q4_K_M (9.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.