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 DiffusionGemma family. At Q4_K_M it needs about 17.16 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
2 likesSpecifications
- Publisher
- meshllm
- Family
- DiffusionGemma
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 12.2 GB | — | 11.05 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 13.9 GB | — | 12.68 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 17.2 GB | — | 15.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 20.4 GB | — | 18.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 23.6 GB | — | 21.45 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 28.6 GB | — | 26.00 GB | 8-bit quantization, near-lossless |
| 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?
Q4_K_M · 17.2 GBDiffusiongemma 26B A4B IT Q4 K M Layers (Q4_K_M) requires 17.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Diffusiongemma 26B A4B IT Q4 K M Layers?
Q4_K_M · 17.2 GB41 devices with unified memory can run Diffusiongemma 26B A4B IT Q4 K M Layers, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— 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 17.2 GB of VRAM at Q4_K_M, or 57.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 26B × 4.8 bits ÷ 8 = 15.6 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M17.2 GB- Can NVIDIA GeForce RTX 4090 run Diffusiongemma 26B A4B IT Q4 K M Layers?
Yes, at Q6_K (23.6 GB) or lower. Higher quantizations like Q8_0 (28.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Diffusiongemma 26B A4B IT Q4 K M Layers?
For Diffusiongemma 26B A4B IT Q4 K M Layers, Q4_K_M (17.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (20.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.2 GB.
VRAM requirement by quantization
Q2_K12.2 GBQ4_K_M ★17.2 GBQ5_K_M20.4 GBQ6_K23.6 GBQ8_028.6 GBBF1657.2 GB★ Recommended — best balance of quality and VRAM usage.
- 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 12.2 GB at Q2_K, 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 Q4_K_M quantization it needs 17.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Diffusiongemma 26B A4B IT Q4 K M Layers?
At Q4_K_M, Diffusiongemma 26B A4B IT Q4 K M Layers can reach ~99 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~150 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 ÷ 17.2 × 0.65 = ~318 tok/s
Estimated speed at Q4_K_M (17.2 GB)
~318 tok/s~150 tok/s~318 tok/s~288 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 Q4_K_M, the download is about 15.60 GB. The full-precision BF16 version is 52.00 GB. The smallest option (Q2_K) is 11.05 GB.
- Which GPUs can run Diffusiongemma 26B A4B IT Q4 K M Layers?
8 consumer GPUs can run Diffusiongemma 26B A4B IT Q4 K M Layers at Q4_K_M (17.2 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Diffusiongemma 26B A4B IT Q4 K M Layers?
41 devices with unified memory can run Diffusiongemma 26B A4B IT Q4 K M Layers at Q4_K_M (17.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.