Bonsai 2 27B Mtp — Hardware Requirements & GPU Compatibility
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Bonsai 2 27B Mtp is a 27B-parameter open language model from decent-jawfish. At Q4_K_M it needs about 17.82 GB of VRAM — see which GPUs and Macs can run it below.
2.7K downloads
10 likesSpecifications
- Publisher
- decent-jawfish
- Parameters
- 27B
- Release Date
- 2026-09-18
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Bonsai 2 27B Mtp Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 12.6 GB | — | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 14.5 GB | — | 13.16 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 17.8 GB | — | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 21.2 GB | — | 19.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 24.5 GB | — | 22.27 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 29.7 GB | — | 27.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 59.4 GB | — | 54.00 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 Bonsai 2 27B Mtp?
Q4_K_M · 17.8 GBBonsai 2 27B Mtp (Q4_K_M) requires 17.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ 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 Bonsai 2 27B Mtp?
Q4_K_M · 17.8 GB41 devices with unified memory can run Bonsai 2 27B Mtp, 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 Bonsai 2 27B Mtp need?
Bonsai 2 27B Mtp requires 17.8 GB of VRAM at Q4_K_M, or 59.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M17.8 GB- Can NVIDIA GeForce RTX 4090 run Bonsai 2 27B Mtp?
Yes, at Q5_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Bonsai 2 27B Mtp?
For Bonsai 2 27B Mtp, Q4_K_M (17.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (21.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.6 GB.
VRAM requirement by quantization
Q2_K12.6 GBQ4_K_M ★17.8 GBQ5_K_M21.2 GBQ6_K24.5 GBQ8_029.7 GBBF1659.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Bonsai 2 27B Mtp on a Mac?
Bonsai 2 27B Mtp requires at least 12.6 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 Bonsai 2 27B Mtp locally?
Yes — Bonsai 2 27B Mtp can run locally on consumer hardware. At Q4_K_M quantization it needs 17.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Bonsai 2 27B Mtp?
At Q4_K_M, Bonsai 2 27B Mtp can reach ~269 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.8 × 0.65 = ~292 tok/s
Estimated speed at Q4_K_M (17.8 GB)
~292 tok/s~37 tok/s~292 tok/s~269 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Bonsai 2 27B Mtp?
At Q4_K_M, the download is about 16.20 GB. The full-precision BF16 version is 54.00 GB. The smallest option (Q2_K) is 11.47 GB.
- Which GPUs can run Bonsai 2 27B Mtp?
8 consumer GPUs can run Bonsai 2 27B Mtp at Q4_K_M (17.8 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 Bonsai 2 27B Mtp?
41 devices with unified memory can run Bonsai 2 27B Mtp at Q4_K_M (17.8 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.