Grug 27B v2 — Hardware Requirements & GPU Compatibility
ChatFunctions
Grug 27B v2 is a 27.4B-parameter open language model from ProCreations. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 17.16 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (Q3_K_M · 32K)Best on 24 GB (Q4_K_M · ~38 tok/s · 128K)Macs from 32 GB (~5 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Grug 27B v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/ProCreations/grug-27b-v2-gguf:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 6.4 GB free · 16K context
Download: grug-27b-v2-Q4_K_M.gguf17.00 GBProCreations
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3090 (24 GB) (6 GB headroom) · Q4_K_M
- Generation speed
- ~36 tok/s
- generation speed
- Cost per 1M output tokens
- $1.33
- per 1M output tokens
How Much VRAM Does Grug 27B v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 12.4 GB | 13.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~53 tok/s | |
| 14.1 GB | 15.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~47 tok/s | |
| 17.2 GB | 18.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~38 tok/s | |
| 20.2 GB | 21.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~32 tok/s | |
| 23.3 GB | 24.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~28 tok/s | |
| 28.1 GB | 29.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 28.6 GB | |
| 55.5 GB | 56.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 55.9 GB |
Selected Q4_K_M
4-bit medium quantization17.6GBat 16K context
- Weights
- 16.4 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 6.4 GB free · ~38 tok/s
≥50 tok/s needs ≥1.32 TB/s · RTX 5090 ~68
Which Hardware Can Run Grug 27B v2?
Q4_K_M · 17.2 GBGrug 27B v2 (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. Using the full 262K context window can add up to 13.9 GB, bringing total usage to 31.0 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 125 devices with unified memory can run Grug 27B v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M5 Pro (24 GB).
20 GB
fits · 2.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~28Simulate output on RX 7900 XT32–40 GB
fits · 14.4 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~68Simulate output on RTX 5090M5 Max36 GB~19Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~8Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate 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 |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 121.5 | 17.9 GB | ~38 | same size |
| Qwen3.6 27BAlibaba | 111.4 | 17.9 GB | ~38 | same size |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | same size |
| Gemma 3 27B ITGoogle | 79.3 | 21.7 GB | ~36 | same size |
| Gemma 4 26B A4BGoogle | — | 16.9 GB | ~153 | same size |
| ERNIE 4.5 VL 28B A3B PTBaidu | — | 60 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- ProCreations
- Parameters
- 27.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-12
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Grug 27B v2 need?
Grug 27B v2 requires 17.2 GB of VRAM at Q4_K_M, or 55.5 GB at BF16. Full 262K context adds up to 13.9 GB (31.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.4B × 4.8 bits ÷ 8 = 16.4 GB
KV Cache + Overhead ≈ 0.8 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 ≈ 14.6 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Grug 27B v2?
Yes, at Q6_K (23.3 GB) or lower. Higher quantizations like Q8_0 (28.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Grug 27B v2?
For Grug 27B v2, Q4_K_M (17.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (20.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Grug 27B v2 on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Grug 27B v2 is Mac Mini M1 (16 GB) at Q2_K; 135 of the 156 Macs we list can run it at some quantization. For Q4_K_M (17.2 GB) you need a Mac with more unified memory.
Can I run Grug 27B v2 locally?
Yes — Grug 27B v2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Grug 27B v2?
At Q4_K_M, Grug 27B v2 can reach ~280 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.16 × 0.65 = ~303 tok/s
Estimated speed at Q4_K_M (17.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Grug 27B v2?
At Q4_K_M, the download is about 16.41 GB. The full-precision BF16 version is 54.71 GB. The smallest option (Q2_K) is 11.63 GB.
Which GPUs can run Grug 27B v2?
8 consumer GPUs can run Grug 27B v2 at Q4_K_M. 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 Grug 27B v2?
125 devices with unified memory can run Grug 27B v2 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.