H2ovl Mississippi 2B — Hardware Requirements & GPU Compatibility
ChatVision
H2OVL-Mississippi-2B is H2O.ai's 2.15-billion-parameter vision-language model, extending their H2O-Danube2-1.8B chat model with a vision encoder for multimodal tasks. It handles image captioning, visual question answering, and document understanding, positioned as an efficient, general-purpose OCR and document-AI model. H2O.ai trained it on 17 million image-text pairs and reports it competing with other 2B-class models like Qwen2-VL-2B and InternVL2-2B on public benchmarks. At this size, it runs easily on a single consumer GPU, even unquantized. Context length is 8,192 tokens, inherited from its Danube2 language backbone. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2024, it is part of H2O.ai's Mississippi family of open document-AI and OCR-focused vision-language models.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 16K)Best on 24 GB (BF16 · ~138 tok/s · 32K)Macs from 16 GB (BF16 · ~30 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run H2ovl Mississippi 2B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve h2oai/h2ovl-mississippi-2b --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (5 GB headroom) · BF16
- Generation speed
- ~49 tok/s
- generation speed
- Cost per 1M output tokens
- $0.39
- per 1M output tokens
How Much VRAM Does H2ovl Mississippi 2B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.7 GB | 8.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~138 tok/s |
Selected BF16
Brain floating point 166.5GBat 16K context
- Weights
- 4.2 GB
- KV cache
- 2.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.5 GB free · ~138 tok/s
≥50 tok/s needs ≥364 GB/s · RTX 5090 ~246, RTX 3090 Ti ~138
Which Hardware Can Run H2ovl Mississippi 2B?
BF16 · 4.7 GBH2ovl Mississippi 2B (BF16) 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. 151 devices with unified memory can run H2ovl Mississippi 2B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.5 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~104Simulate output on RTX 3080RTX 2080 Ti~85Simulate output on RTX 2080 TiRTX 3070 Ti~83Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 13.5 GB free · up to 32K ctx
1 GPU
RX 7900 XT~101Simulate output on RX 7900 XT32–40 GB
fits · 25.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~246Simulate output on RTX 5090M5 Max36 GB~68Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~28Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 2BAlibaba | — | 1.9 GB | ~370 | same size |
| Qwen3 VL 2B InstructAlibaba | — | 3.5 GB | ~362 | same size |
| Qwen2 VL 2B InstructAlibaba | — | 2.1 GB | ~390 | same size |
| Kimi K3 DSparkRadixArk | — | 2.2 GB | ~381 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
| MiniCPM 2B Sft BF16OpenBMB | — | 3 GB | ~291 | same size |
Specifications
- Publisher
- h2oai
- Parameters
- 2.2B
- Architecture
- H2OVLChatModel
- Release Date
- 2024-10-15
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does H2ovl Mississippi 2B need?
H2ovl Mississippi 2B requires 4.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.2B × 16 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.4 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 H2ovl Mississippi 2B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run H2ovl Mississippi 2B. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 4.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run H2ovl Mississippi 2B locally?
Yes — H2ovl Mississippi 2B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is H2ovl Mississippi 2B?
At BF16, H2ovl Mississippi 2B can reach ~1013 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~138 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 ÷ 4.74 × 0.65 = ~1097 tok/s
Estimated speed at BF16 (4.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of H2ovl Mississippi 2B?
At BF16, the download is about 4.30 GB.
Which GPUs can run H2ovl Mississippi 2B?
52 consumer GPUs can run H2ovl Mississippi 2B at BF16. 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 H2ovl Mississippi 2B?
151 devices with unified memory can run H2ovl Mississippi 2B at BF16 (4.7 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.