VulnLLM R 7B — Hardware Requirements & GPU Compatibility
ChatReasoning
VulnLLM R 7B is a security-focused model developed by UCSB-SURFI, built on the Qwen2.5-7B base and fine-tuned specifically for vulnerability analysis and security reasoning. With 7.6 billion parameters, it targets tasks like identifying code vulnerabilities, explaining security flaws, and reasoning about attack vectors. This model fills a niche for security researchers and developers who want a locally-hosted assistant for code auditing and vulnerability assessment without sending sensitive code to external APIs. Its specialized training gives it an edge over general-purpose models on security-related tasks, though it is not a replacement for professional security tools. Runs on consumer GPUs with 8 GB of VRAM at typical quantization levels.
59.7K downloads
179 likesSpecifications
- Publisher
- UCSB-SURFI
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-12-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does VulnLLM R 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 15.7 GB | 17.4 GB | 15.23 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run VulnLLM R 7B?
BF16 · 15.7 GBVulnLLM R 7B (BF16) requires 15.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 17.4 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run VulnLLM R 7B?
BF16 · 15.7 GB27 devices with unified memory can run VulnLLM R 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does VulnLLM R 7B need?
VulnLLM R 7B requires 15.7 GB of VRAM at BF16. Full 33K context adds up to 1.8 GB (17.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 16 bits ÷ 8 = 15.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
BF1615.7 GBBF16 + full context17.4 GB- Can I run VulnLLM R 7B on a Mac?
VulnLLM R 7B requires at least 15.7 GB at BF16, 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 VulnLLM R 7B locally?
Yes — VulnLLM R 7B can run locally on consumer hardware. At BF16 quantization it needs 15.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is VulnLLM R 7B?
At BF16, VulnLLM R 7B can reach ~186 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~42 tok/s. 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 ÷ 15.7 × 0.55 = ~186 tok/s
Estimated speed at BF16 (15.7 GB)
AMD Instinct MI300X~186 tok/sNVIDIA GeForce RTX 4090~42 tok/sNVIDIA H100 SXM~139 tok/sAMD Instinct MI250X~115 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of VulnLLM R 7B?
At BF16, the download is about 15.23 GB.