Diffusiongemma 26B A4B IT Q4 K M Layers — Hardware Requirements & GPU Compatibility
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Diffusiongemma 26B A4B IT Q4 K M Layers is a 26B-parameter open language model from meshllm in the Gemma family. At BF16 it needs about 57.20 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
2 likesSpecifications
- Publisher
- meshllm
- Family
- Gemma
- Parameters
- 26B
- Release Date
- 2026-06-10
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How Much VRAM Does Diffusiongemma 26B A4B IT Q4 K M Layers Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 57.2 GB | — | 52.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Diffusiongemma 26B A4B IT Q4 K M Layers?
BF16 · 57.2 GBDiffusiongemma 26B A4B IT Q4 K M Layers (BF16) requires 57.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 75+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Diffusiongemma 26B A4B IT Q4 K M Layers?
BF16 · 57.2 GB8 devices with unified memory can run Diffusiongemma 26B A4B IT Q4 K M Layers, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Diffusiongemma 26B A4B IT Q4 K M Layers need?
Diffusiongemma 26B A4B IT Q4 K M Layers requires 57.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 26B × 16 bits ÷ 8 = 52 GB
KV Cache + Overhead ≈ 5.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1657.2 GB- Can NVIDIA GeForce RTX 5090 run Diffusiongemma 26B A4B IT Q4 K M Layers?
No — Diffusiongemma 26B A4B IT Q4 K M Layers requires at least 57.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Diffusiongemma 26B A4B IT Q4 K M Layers on a Mac?
Diffusiongemma 26B A4B IT Q4 K M Layers requires at least 57.2 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Diffusiongemma 26B A4B IT Q4 K M Layers locally?
Yes — Diffusiongemma 26B A4B IT Q4 K M Layers can run locally on consumer hardware. At BF16 quantization it needs 57.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Diffusiongemma 26B A4B IT Q4 K M Layers?
At BF16, Diffusiongemma 26B A4B IT Q4 K M Layers can reach ~51 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 57.2 × 0.55 = ~51 tok/s
Estimated speed at BF16 (57.2 GB)
~51 tok/s~38 tok/s~32 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Diffusiongemma 26B A4B IT Q4 K M Layers?
At BF16, the download is about 52.00 GB.
- Which GPUs can run Diffusiongemma 26B A4B IT Q4 K M Layers?
No single consumer GPU has enough VRAM to run Diffusiongemma 26B A4B IT Q4 K M Layers at BF16 (57.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Diffusiongemma 26B A4B IT Q4 K M Layers?
8 devices with unified memory can run Diffusiongemma 26B A4B IT Q4 K M Layers at BF16 (57.2 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), Mac Studio M4 Max (64 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.