LensVLM 9B — Hardware Requirements & GPU Compatibility
VisionFunctions
LensVLM-9B is Apple's research vision-language model for efficient long-document understanding, built on a 9.4-billion-parameter Qwen3.5-9B backbone. Rather than feeding a document's full text into the context window, it scans compressed images of the text and uses learned tools to selectively expand only the pages relevant to a query back into their uncompressed form, at configurable 5x, 10x, or 15x compression ratios, letting it reason over long documents while processing far less raw context per query. It is a research model released alongside its arXiv paper and reference code, not a product, and is not described as instruction-tuned for general chat. At 9.4 billion parameters it fits on a single consumer GPU, especially once quantized. Context length is 262,144 tokens. It is released under the Apple Machine Learning Research Model License, a custom license restricted strictly to non-commercial research purposes, with no commercial exploitation permitted. It was published in September 2026.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~106 tok/s · 256K)Macs from 8 GB (IQ3_XXS · ~17 tok/s)
apple-amlr
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run LensVLM 9B
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 hf.co/bartowski/LensVLM-9B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.5 GB free · 16K context
Download: LensVLM-9B-Q4_K_M.gguf5.84 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~96 tok/s
- generation speed
- Cost per 1M output tokens
- $0.36
- per 1M output tokens
How Much VRAM Does LensVLM 9B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.6 GB | 5.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~143 tok/s | |
| 4.7 GB | 5.5 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~140 tok/s | |
| 5.2 GB | 6 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~127 tok/s | |
| 5.3 GB | 6.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~124 tok/s | |
| 6.2 GB | 7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~106 tok/s | |
| 7.3 GB | 8.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~90 tok/s | |
| 8.3 GB | 9.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~79 tok/s | |
| 10.0 GB | 10.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~66 tok/s |
Selected Q4_K_M
4-bit medium quantization6.5GBat 16K context
- Weights
- 5.6 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.5 GB free · ~106 tok/s
≥50 tok/s needs ≥477 GB/s · RTX 5090 ~188, RTX 3090 Ti ~106
Which Hardware Can Run LensVLM 9B?
Q4_K_M · 6.2 GBLensVLM 9B (Q4_K_M) requires 6.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 262K context window can add up to 8.3 GB, bringing total usage to 14.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run LensVLM 9B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.5 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~80Simulate output on RTX 3080RTX 2080 Ti~65Simulate output on RTX 2080 TiRTX 3070 Ti~64Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.5 GB free · up to 256K ctx
1 GPU
RX 7900 XT~77Simulate output on RX 7900 XT32–40 GB
fits · 25.5 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~188Simulate output on RTX 5090M5 Max36 GB~52Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | same size |
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
| Qwen3 VL 8B InstructAlibaba | — | 8 GB | ~112 | same size |
Specifications
- Publisher
- Apple
- Parameters
- 9.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-21
- License
- apple-amlr
Frequently Asked Questions
How much VRAM does LensVLM 9B need?
LensVLM 9B requires 6.2 GB of VRAM at Q4_K_M, or 19.4 GB at BF16. Full 262K context adds up to 8.3 GB (14.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 4.8 bits ÷ 8 = 5.6 GB
KV Cache + Overhead ≈ 0.6 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.
KV Cache + Overhead ≈ 8.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for LensVLM 9B?
For LensVLM 9B, Q4_K_M (6.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run LensVLM 9B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run LensVLM 9B, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.2 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run LensVLM 9B locally?
Yes — LensVLM 9B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is LensVLM 9B?
At Q4_K_M, LensVLM 9B can reach ~773 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~106 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 ÷ 6.21 × 0.65 = ~837 tok/s
Estimated speed at Q4_K_M (6.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of LensVLM 9B?
At Q4_K_M, the download is about 5.65 GB. The full-precision BF16 version is 18.82 GB. The smallest option (IQ2_XXS) is 2.59 GB.
Which GPUs can run LensVLM 9B?
52 consumer GPUs can run LensVLM 9B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run LensVLM 9B?
151 devices with unified memory can run LensVLM 9B at Q4_K_M (6.2 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.