llmrun
org2aiGemma4ForConditionalGeneration

Wald 26B A4B — Hardware Requirements & GPU Compatibility

ChatReasoningFunctions

Wald 26B A4B is a 25.8B-parameter open language model from org2ai. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 16.13 GB of VRAM — see which GPUs and Macs can run it below.

25.8B params · MoE4.4B active262K contextApache 2.037.2K downloads3 likes
Based on Gemma 4 26B A4B IT
S

Runs great on a 24 GB GPU

Runs from12 GB Q2_K · 16KBest on24 GB Q4_K_M · ~171 tok/s · 128KMacs from16 GB Q2_K · ~50 tok/s

Apache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Wald 26B A4B

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

vLLM
one command · needs vLLM installed
$vllm serve org2ai/Wald-26B-A4B --max-model-len 16384

vLLM repo with its own quantisation

Download: Weights on Hugging Face

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on RTX 3090 (24 GB) (7 GB headroom) · Q4_K_M

Generation speed
~162 tok/s
generation speed
Cost per 1M output tokens
$0.31
per 1M output tokens
Deployon Vast.aiBest valuefrom $0.18/hr (opens in new tab)
Compare GPUs →
or

How Much VRAM Does Wald 26B A4B Need?

Select a quantization level; the listed hardware adheres to it.

QuantizationVRAM+32KOn RTX 4090 (reference)
11.6 GB12.5 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 256K context · ~214 tok/s
13.2 GB14.1 GB3-bit medium — 3-bit medium quantizationFits: 256K context · ~197 tok/s
16.1 GB17 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 128K context · ~171 tok/s
19.0 GB19.9 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffFits: 128K context · ~152 tok/s
21.9 GB22.8 GB6-bit, very good — 6-bit quantization, very good qualityFits: 64K context · ~136 tok/s
26.4 GB27.4 GB8-bit, near-lossless — 8-bit quantization, near-losslessDoes not fit: needs 26.8 GB
52.3 GB53.2 GB16-bit · bfloat — Brain floating point 16 — preferred for trainingDoes not fit: needs 52.6 GB
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

16.5GBat 16K context

Weights
15.5 GB
KV cache
0.7 GB
Overhead
0.3 GB

Fits RTX 4090 (reference) · 7.5 GB free · ~171 tok/s

≥50 tok/s needs ≥230 GB/s · RTX 5090 ~250, RTX 3090 Ti ~171

Which Hardware Can Run Wald 26B A4B?

Q4_K_M · 16.1 GB

Wald 26B A4B (Q4_K_M) requires 16.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 262K context window can add up to 8.7 GB, bringing total usage to 24.8 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Wald 26B A4B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8 GB · RTX 4060Doesn't fit — smallest quant needs 12 GB8 GB tier →
12 GB · RTX 3060 12GBQ2_K · 12 GB · ~99 tok/s · 16K12 GB tier →
16 GB · RTX 5060 Ti 16GBQ3_K_M · 13.6 GB · ~107 tok/s · 64K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 16.5 GB · ~171 tok/s · 128K24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 16.5 GB · ~26 tok/s · 256KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 16.5 GB · ~76 tok/s · 256KMacs →

Alternatives to Wald 26B A4B

Similar models

6
ModelScoreVRAMtok/sWhy it is similar
Qwen3.8 27BAlibaba121.917.9 GB~38same size
Qwen3.6 27BAlibaba111.217.9 GB~38same size
Qwen3.5 27BAlibaba103.817.9 GB~38same size
Magistral Small 2509Mistral AI85.618.1 GB~43same size
LFM2.5 24B A2BLiquid AI—15 GB~221same size
Gemma 4 26B A4BGoogle—16.5 GB~171same size

Specifications

Publisher
org2ai
Parameters
25.8B
Active parameters
4.4B per token
Architecture
Gemma4ForConditionalGeneration
Context Length
262,144 tokens
Vocabulary Size
262,144
Release Date
2026-10-09
License
Apache 2.0

Resources

Frequently Asked Questions

How much VRAM does Wald 26B A4B need?

Wald 26B A4B requires 16.1 GB of VRAM at Q4_K_M, or 52.3 GB at BF16. Full 262K context adds up to 8.7 GB (24.8 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 25.8B × 4.8 bits ÷ 8 = 15.5 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 ≈ 9.3 GB (at full 262K context)

VRAM usage by quantization

16.1 GB
24.8 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Wald 26B A4B?

Yes, at Q6_K (21.9 GB) or lower. Higher quantizations like Q8_0 (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Wald 26B A4B?

For Wald 26B A4B, Q4_K_M (16.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (19.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.6 GB.

VRAM requirement by quantization

Q2_K
11.6 GB
Q4_K_M ★
16.1 GB
Q5_K_M
19.0 GB
Q6_K
21.9 GB
Q8_0
26.4 GB
BF16
52.3 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Wald 26B A4B on a Mac?

Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Wald 26B A4B, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 16.1 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).

Can I run Wald 26B A4B locally?

Yes — Wald 26B A4B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Wald 26B A4B?

At Q4_K_M, Wald 26B A4B can reach ~152 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~171 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)

Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.

Example: NVIDIA B200 → 2.7 GB active ÷ (8000 × 0.65) = 0.53 ms, plus 30 layers × 0.055 ms = 1.65 ms, so 1000 ÷ 2.18 ms = ~459 tok/s

Estimated speed at Q4_K_M (16.1 GB)

~459 tok/s
~171 tok/s
~459 tok/s
~398 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Wald 26B A4B?

At Q4_K_M, the download is about 15.48 GB. The full-precision BF16 version is 51.61 GB. The smallest option (Q2_K) is 10.97 GB.

Which GPUs can run Wald 26B A4B?

8 consumer GPUs can run Wald 26B A4B at Q4_K_M. 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 Wald 26B A4B?

122 devices with unified memory can run Wald 26B A4B at Q4_K_M (16.1 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.