Llava V1.6 Mistral 7B HF — Hardware Requirements & GPU Compatibility
Vision
LLaVA-1.6 (also called LLaVA-NeXT) Mistral 7B is a 7.6-billion-parameter vision-language model that pairs a pretrained vision encoder with a Mistral-7B language backbone for multimodal chat, image captioning, and visual question answering. It improves on LLaVA-1.5 with higher and dynamic input image resolution, a larger and more diverse visual instruction-tuning mixture, and a commercially friendlier base model, together boosting its OCR and common-sense reasoning. This checkpoint is the Hugging Face Transformers-format port of the original research release, hosted under the community llava-hf organization rather than the original authors' account. At 7.6 billion parameters it fits comfortably on a single consumer GPU, especially once quantized. Context length is 32,768 tokens, inherited from the underlying Mistral-7B-Instruct-v0.2 backbone. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. It was published in February 2024, alongside sibling LLaVA-NeXT checkpoints built on Vicuna and Nous-Hermes-2-Yi-34B backbones.
510.7K downloads
315 likesSpecifications
- Publisher
- llava-hf
- Family
- Mistral
- Parameters
- 7.6B
- Architecture
- LlavaNextForConditionalGeneration
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-02-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Llava V1.6 Mistral 7B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16est. | 16.00 | 16.6 GB | — | 15.13 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 V1.6 Mistral 7B HF?
FP16 · 16.6 GBLlava V1.6 Mistral 7B HF (FP16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llava V1.6 Mistral 7B HF?
FP16 · 16.6 GB41 devices with unified memory can run Llava V1.6 Mistral 7B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Llava V1.6 Mistral 7B HF need?
Llava V1.6 Mistral 7B HF requires 16.6 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 16 bits ÷ 8 = 15.1 GB
KV Cache + Overhead ≈ 1.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP1616.6 GB- Can I run Llava V1.6 Mistral 7B HF on a Mac?
Llava V1.6 Mistral 7B HF requires at least 16.6 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 Llava V1.6 Mistral 7B HF locally?
Yes — Llava V1.6 Mistral 7B HF can run locally on consumer hardware. At FP16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llava V1.6 Mistral 7B HF?
At FP16, Llava V1.6 Mistral 7B HF can reach ~288 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 16.6 × 0.65 = ~312 tok/s
Estimated speed at FP16 (16.6 GB)
~312 tok/s~39 tok/s~312 tok/s~288 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llava V1.6 Mistral 7B HF?
At FP16, the download is about 15.13 GB.
- Which GPUs can run Llava V1.6 Mistral 7B HF?
8 consumer GPUs can run Llava V1.6 Mistral 7B HF at FP16 (16.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Llava V1.6 Mistral 7B HF?
41 devices with unified memory can run Llava V1.6 Mistral 7B HF at FP16 (16.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.