ReaderLM v2 — Hardware Requirements & GPU Compatibility
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ReaderLM v2 is a 1.5B-parameter open language model from jinaai. It supports a context window of up to 512,768 tokens. At Q4_K_M it needs about 1.28 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~512 tok/s · 501K)Macs from 8 GB (Q4_K_M · ~56 tok/s)
CC BY-NC 4.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run ReaderLM v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/ReaderLM-v2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 22.3 GB free · 16K context
Download: ReaderLM-v2.Q4_K_M.gguf1.12 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~183 tok/s
- generation speed
- Cost per 1M output tokens
- $0.13
- per 1M output tokens
How Much VRAM Does ReaderLM v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.0 GB | 1.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 501K context · ~649 tok/s | |
| 1.0 GB | 1.9 GB | 3-bit small — 3-bit small quantization | Fits: 501K context · ~636 tok/s | |
| 1.1 GB | 2 GB | 3-bit medium — 3-bit medium quantization | Fits: 501K context · ~590 tok/s | |
| 1.1 GB | 2 GB | 3-bit large — 3-bit large quantization | Fits: 501K context · ~570 tok/s | |
| 1.3 GB | 2.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 501K context · ~512 tok/s | |
| 1.5 GB | 2.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 501K context · ~449 tok/s | |
| 1.6 GB | 2.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 501K context · ~402 tok/s | |
| 1.9 GB | 2.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 501K context · ~345 tok/s |
Selected Q4_K_M
4-bit medium quantization1.7GBat 16K context
- Weights
- 0.9 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.3 GB free · ~512 tok/s
≥50 tok/s needs ≥98 GB/s · RTX 5090 ~910, RTX 3090 Ti ~512
Which Hardware Can Run ReaderLM v2?
Q4_K_M · 1.3 GBReaderLM v2 (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 513K context window can add up to 14.7 GB, bringing total usage to 15.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run ReaderLM v2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.3 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~386Simulate output on RTX 3080RTX 2080 Ti~313Simulate output on RTX 2080 TiRTX 3070 Ti~309Simulate output on RTX 3070 TiM3*8 GB~56Simulate output on iMac 24" M3 (8 GB)Orin Nano~52Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~37Simulate output on iPhone 1720 GB
fits · 18.3 GB free · up to 501K ctx
1 GPU
RX 7900 XT~375Simulate output on RX 7900 XT32–40 GB
fits · 30.3 GB free · up to 501K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~910Simulate output on RTX 5090M5 Max36 GB~252Simulate output on Mac Studio M5 Max (36 GB)M532 GB~84Simulate output on MacBook Air 13" M5 (32 GB)Orin~104Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~69Simulate 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 |
|---|---|---|---|---|
| MiniCPM V 4.7 1BOpenBMB | — | 1.6 GB | ~512 | same size |
| Granite 4.0 1BIBM | — | 2.6 GB | ~452 | same size |
| LFM2 VL 1.6BLiquid AI | — | 1.5 GB | ~496 | same size |
| Qwen Base Invoicev1.01 1.5BLaaP-ai | — | 1.7 GB | ~512 | same size |
| OLMo 2 0425 1B InstructAllen AI | — | 1.7 GB | ~449 | same size |
| OpenMath Nemotron 1.5BNVIDIA | — | 1.7 GB | ~512 | same size |
Specifications
- Publisher
- jinaai
- Parameters
- 1.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 512,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-01-13
- License
- CC BY-NC 4.0
Frequently Asked Questions
How much VRAM does ReaderLM v2 need?
ReaderLM v2 requires 1.3 GB of VRAM at Q4_K_M, or 3.5 GB at BF16. Full 513K context adds up to 14.7 GB (15.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 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.
KV Cache + Overhead ≈ 15 GB (at full 513K context)
VRAM usage by quantization
What's the best quantization for ReaderLM v2?
For ReaderLM v2, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run ReaderLM v2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run ReaderLM v2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run ReaderLM v2 locally?
Yes — ReaderLM v2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is ReaderLM v2?
At Q4_K_M, ReaderLM v2 can reach ~3750 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~512 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 ÷ 1.28 × 0.65 = ~4063 tok/s
Estimated speed at Q4_K_M (1.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of ReaderLM v2?
At Q4_K_M, the download is about 0.93 GB. The full-precision BF16 version is 3.09 GB. The smallest option (Q2_K) is 0.66 GB.
Which GPUs can run ReaderLM v2?
52 consumer GPUs can run ReaderLM v2 at Q4_K_M. 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 ReaderLM v2?
163 devices with unified memory can run ReaderLM v2 at Q4_K_M (1.3 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.