Llava 1.5 7B HF — Hardware Requirements & GPU Compatibility
Vision
LLaVA-1.5-7B is a 7-billion-parameter vision-language chat model made by fine-tuning Vicuna-7B, itself based on Llama 2, on GPT-generated multimodal instruction data, paired with a CLIP ViT-L/14 vision encoder via an MLP projector. It answers questions about images, describes visual content, and follows multi-turn multimodal instructions, and was one of the first widely used open vision-language chat models. At 7B it runs comfortably on a single consumer GPU once quantized. Context length is limited to 4,096 tokens, short by current standards given its 2023-era Llama 2 backbone. It is released under the Llama 2 Community License, permitting commercial use with some restrictions, including a separate license for very large companies; published in December 2023. Unlike newer small vision-language models, LLaVA-1.5 uses a simple architecture with no token-compression for image patches.
1.6M downloads
375 likesSpecifications
- Publisher
- llava-hf
- Family
- LLaVA
- Parameters
- 7.1B
- Architecture
- LlavaForConditionalGeneration
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2023-12-05
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Llava 1.5 7B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.3 GB | — | 3.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.8 GB | — | 3.44 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 4.7 GB | — | 4.24 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 5.5 GB | — | 5.03 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 6.4 GB | — | 5.83 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 7.8 GB | — | 7.06 GB | 8-bit quantization, near-lossless |
| FP16est. | 16.00 | 15.5 GB | — | 14.13 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 Llava 1.5 7B HF?
Q4_K_M · 4.7 GBLlava 1.5 7B HF (Q4_K_M) requires 4.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llava 1.5 7B HF?
Q4_K_M · 4.7 GB59 devices with unified memory can run Llava 1.5 7B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does Llava 1.5 7B HF need?
Llava 1.5 7B HF requires 4.7 GB of VRAM at Q4_K_M, or 15.5 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.1B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.7 GB- What's the best quantization for Llava 1.5 7B HF?
For Llava 1.5 7B HF, Q4_K_M (4.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GBQ4_K_M ★4.7 GBQ5_K_M5.5 GBQ6_K6.4 GBQ8_07.8 GBFP1615.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llava 1.5 7B HF on a Mac?
Llava 1.5 7B HF requires at least 3.3 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 Llava 1.5 7B HF locally?
Yes — Llava 1.5 7B HF can run locally on consumer hardware. At Q4_K_M quantization it needs 4.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llava 1.5 7B HF?
At Q4_K_M, Llava 1.5 7B HF can reach ~1030 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~141 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 ÷ 4.7 × 0.65 = ~1116 tok/s
Estimated speed at Q4_K_M (4.7 GB)
~1116 tok/s~141 tok/s~1116 tok/s~1030 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llava 1.5 7B HF?
At Q4_K_M, the download is about 4.24 GB. The full-precision FP16 version is 14.13 GB. The smallest option (Q2_K) is 3.00 GB.
- Which GPUs can run Llava 1.5 7B HF?
52 consumer GPUs can run Llava 1.5 7B HF at Q4_K_M (4.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llava 1.5 7B HF?
59 devices with unified memory can run Llava 1.5 7B HF at Q4_K_M (4.7 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.