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.
Runs well on a 24 GB GPU
Runs on 24 GB (FP16 · ~39 tok/s · 32K)Macs from 24 GB (FP16 · ~13 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llava V1.6 Mistral 7B HF
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run llava:7b-v1.6-mistral-fp16FP16 fits fully on the RTX 4090 · 4.8 GB free · 16K context
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (4 GB headroom) · FP16
- Generation speed
- ~37 tok/s
- generation speed
- Cost per 1M output tokens
- $1.51
- per 1M output tokens
How Much VRAM Does Llava V1.6 Mistral 7B HF Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 16.6 GB | 22.1 GB | 16-bit · full — Full half-precision — baseline for inference | Fits: 32K context · ~39 tok/s |
Selected FP16
Full half-precision19.2GBat 16K context
- Weights
- 16.0 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.8 GB free · ~39 tok/s
≥50 tok/s needs ≥1.28 TB/s · RTX 5090 ~70
Which Hardware 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. 122 devices with unified memory can run Llava V1.6 Mistral 7B HF, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 0.8 GB free · up to 16K ctx
1 GPU
RX 7900 XT~29Simulate output on RX 7900 XT32–40 GB
fits · 12.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~70Simulate output on RTX 5090M5 Max36 GB~19Simulate output on Mac Studio M5 Max (36 GB)M532 GB~7Simulate output on MacBook Air 13" M5 (32 GB)Orin~8Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at FP16
Alternatives to Llava V1.6 Mistral 7B HF
Mistral family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Gemma 3n E4B ITGoogle | — | 7.7 GB | ~127 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Llava V1.6 Mistral 7B HF on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Llava V1.6 Mistral 7B HF. Apple Silicon uses unified memory, so the model shares RAM with the system. At FP16 you need at least 16.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llava V1.6 Mistral 7B HF locally?
Yes — Llava V1.6 Mistral 7B HF can run locally on consumer hardware. 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 ÷ 16.65 × 0.65 = ~312 tok/s
Estimated speed at FP16 (16.6 GB)
Real-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. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Llava V1.6 Mistral 7B HF?
122 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.