Wald 4B — Hardware Requirements & GPU Compatibility
ChatReasoningFunctions
Wald 4B is a 4.7B-parameter open language model from org2ai. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 3.26 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 · ~201 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~22 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Wald 4B
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/org2ai/Wald-4B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.6 GB free · 16K context
Download: Wald-4B-v1.2-Q4_K_M.gguf2.71 GBorg2ai
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~72 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does Wald 4B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.5 GB | 3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~267 tok/s | |
| 2.5 GB | 3 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~261 tok/s | |
| 2.7 GB | 3.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~239 tok/s | |
| 2.9 GB | 3.4 GB | 3-bit large — 3-bit large quantization | Fits: 256K context · ~229 tok/s | |
| 3.3 GB | 3.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~201 tok/s | |
| 3.8 GB | 4.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~173 tok/s | |
| 4.3 GB | 4.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~152 tok/s | |
| 5.1 GB | 5.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~128 tok/s |
Selected Q4_K_M
4-bit medium quantization3.4GBat 16K context
- Weights
- 2.8 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~201 tok/s
≥50 tok/s needs ≥251 GB/s · RTX 5090 ~357, RTX 3090 Ti ~201
Which Hardware Can Run Wald 4B?
Q4_K_M · 3.3 GBWald 4B (Q4_K_M) requires 3.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 5.2 GB, bringing total usage to 8.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Wald 4B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.6 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~152Simulate output on RTX 3080RTX 2080 Ti~123Simulate output on RTX 2080 TiRTX 3070 Ti~121Simulate output on RTX 3070 TiM3*8 GB~22Simulate output on iMac 24" M3 (8 GB)Orin Nano~20Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~15Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 256K ctx
1 GPU
RX 7900 XT~147Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~357Simulate output on RTX 5090M5 Max36 GB~99Simulate output on Mac Studio M5 Max (36 GB)M532 GB~33Simulate output on MacBook Air 13" M5 (32 GB)Orin~41Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~27Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 4BAlibaba | 98.5 | 3.4 GB | ~201 | same size |
| Molmo2 4BAllen AI | — | 4.7 GB | ~193 | same size |
| Qwen3 VL 4B InstructAlibaba | — | 4.5 GB | ~208 | same size |
| Agents A1 4BInternScience | — | 3.4 GB | ~205 | same size |
| Qwen3.6 27B MTPLX Optimized SpeedYoussofal | — | 4 GB | ~185 | same size |
Specifications
- Publisher
- org2ai
- Parameters
- 4.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-27
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Wald 4B need?
Wald 4B requires 3.3 GB of VRAM at Q4_K_M, or 9.8 GB at BF16. Full 262K context adds up to 5.2 GB (8.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.7B × 4.8 bits ÷ 8 = 2.8 GB
KV Cache + Overhead ≈ 0.5 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.
KV Cache + Overhead ≈ 5.7 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Wald 4B?
For Wald 4B, Q4_K_M (3.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Wald 4B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Wald 4B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Wald 4B locally?
Yes — Wald 4B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Wald 4B?
At Q4_K_M, Wald 4B can reach ~1472 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~201 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 ÷ 3.26 × 0.65 = ~1595 tok/s
Estimated speed at Q4_K_M (3.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Wald 4B?
At Q4_K_M, the download is about 2.80 GB. The full-precision BF16 version is 9.32 GB. The smallest option (Q2_K) is 1.98 GB.
Which GPUs can run Wald 4B?
52 consumer GPUs can run Wald 4B 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 Wald 4B?
163 devices with unified memory can run Wald 4B at Q4_K_M (3.3 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.