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
- 2025-06-05
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.6 GB | 5.4 GB | 3.24 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.8 GB | 5.5 GB | 3.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.1 GB | 5.9 GB | 3.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.2 GB | 6.0 GB | 3.81 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.0 GB | 6.8 GB | 4.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.8 GB | 7.6 GB | 5.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.7 GB | 8.5 GB | 6.28 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.0 GB | 9.8 GB | 7.62 GB | 8-bit quantization, near-lossless |
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 VulnLLM R 7B?
Q4_K_M · 5.0 GBVulnLLM R 7B (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 6.8 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run VulnLLM R 7B?
Q4_K_M · 5.0 GB59 devices with unified memory can run VulnLLM R 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download VulnLLM R 7B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does VulnLLM R 7B need?
VulnLLM R 7B requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 33K context adds up to 1.8 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M5.0 GBQ4_K_M + full context6.8 GB- What's the best quantization for VulnLLM R 7B?
For VulnLLM R 7B, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.5 GB.
VRAM requirement by quantization
IQ2_XXS2.5 GBIQ3_XS3.6 GBQ4_04.2 GBIQ4_NL4.7 GBQ4_K_M ★5.0 GBBF1615.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run VulnLLM R 7B on a Mac?
VulnLLM R 7B requires at least 2.5 GB at IQ2_XXS, 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 Q4_K_M quantization it needs 5.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is VulnLLM R 7B?
At Q4_K_M, VulnLLM R 7B can reach ~882 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~131 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 ÷ 5.0 × 0.65 = ~1042 tok/s
Estimated speed at Q4_K_M (5.0 GB)
~1042 tok/s~131 tok/s~1042 tok/s~882 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 Q4_K_M, the download is about 4.57 GB. The full-precision BF16 version is 15.23 GB. The smallest option (IQ2_XXS) is 2.09 GB.
- Which GPUs can run VulnLLM R 7B?
50 consumer GPUs can run VulnLLM R 7B at Q4_K_M (5.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run VulnLLM R 7B?
59 devices with unified memory can run VulnLLM R 7B at Q4_K_M (5.0 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.