MiniCPM5 1B Claude Opus Fable5 v2 Thinking — Hardware Requirements & GPU Compatibility
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MiniCPM5 1B Claude Opus Fable5 v2 Thinking is a 1.1B-parameter open language model from GnLOLot in the MiniCPM family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 0.99 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
28 likesSpecifications
- Publisher
- GnLOLot
- Family
- MiniCPM
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 130,560
- Release Date
- 2026-07-13
- License
- Apache 2.0
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How Much VRAM Does MiniCPM5 1B Claude Opus Fable5 v2 Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.8 GB | 3.2 GB | 0.46 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.9 GB | 3.2 GB | 0.53 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.0 GB | 3.4 GB | 0.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.1 GB | 3.5 GB | 0.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.2 GB | 3.6 GB | 0.89 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | 3.8 GB | 1.08 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 2.5 GB | 4.9 GB | 2.16 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 MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
Q4_K_M · 1.0 GBMiniCPM5 1B Claude Opus Fable5 v2 Thinking (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 2.4 GB, bringing total usage to 3.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
Q4_K_M · 1.0 GB59 devices with unified memory can run MiniCPM5 1B Claude Opus Fable5 v2 Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download MiniCPM5 1B Claude Opus Fable5 v2 Thinking
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does MiniCPM5 1B Claude Opus Fable5 v2 Thinking need?
MiniCPM5 1B Claude Opus Fable5 v2 Thinking requires 1.0 GB of VRAM at Q4_K_M, or 2.5 GB at BF16. Full 131K context adds up to 2.4 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context3.4 GB- What's the best quantization for MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
For MiniCPM5 1B Claude Opus Fable5 v2 Thinking, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
Q2_K0.8 GBQ4_K_M ★1.0 GBQ5_K_M1.1 GBQ6_K1.2 GBQ8_01.4 GBBF162.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniCPM5 1B Claude Opus Fable5 v2 Thinking on a Mac?
MiniCPM5 1B Claude Opus Fable5 v2 Thinking requires at least 0.8 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 MiniCPM5 1B Claude Opus Fable5 v2 Thinking locally?
Yes — MiniCPM5 1B Claude Opus Fable5 v2 Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
At Q4_K_M, MiniCPM5 1B Claude Opus Fable5 v2 Thinking can reach ~4444 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~662 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 ÷ 1.0 × 0.65 = ~5253 tok/s
Estimated speed at Q4_K_M (1.0 GB)
~5253 tok/s~662 tok/s~5253 tok/s~4444 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
At Q4_K_M, the download is about 0.65 GB. The full-precision BF16 version is 2.16 GB. The smallest option (Q2_K) is 0.46 GB.
- Which GPUs can run MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
50 consumer GPUs can run MiniCPM5 1B Claude Opus Fable5 v2 Thinking at Q4_K_M (1.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MiniCPM5 1B Claude Opus Fable5 v2 Thinking?
59 devices with unified memory can run MiniCPM5 1B Claude Opus Fable5 v2 Thinking at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.