MiniCPM5 1B Agentic Tooluse Merged FP16 — Hardware Requirements & GPU Compatibility
ChatFunctions
MiniCPM5 1B Agentic Tooluse Merged FP16 is a 1.1B-parameter open language model from ewinregirgojr 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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~662 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~72 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run MiniCPM5 1B Agentic Tooluse Merged FP16
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 22.7 GB free · 16K context
Download: legacy/minicpm5-1b-agentic-tooluse.Q4_K_M.gguf0.69 GBewinregirgojr
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~236 tok/s
- generation speed
- Cost per 1M output tokens
- $0.081
- per 1M output tokens
How Much VRAM Does MiniCPM5 1B Agentic Tooluse Merged FP16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | 1.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~819 tok/s | |
| 0.9 GB | 1.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~762 tok/s | |
| 1.0 GB | 1.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~662 tok/s | |
| 1.1 GB | 1.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~590 tok/s | |
| 1.2 GB | 1.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~533 tok/s | |
| 1.4 GB | 2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~461 tok/s | |
| 2.5 GB | 3.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~262 tok/s |
Selected Q4_K_M
4-bit medium quantization1.3GBat 16K context
- Weights
- 0.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.8 GB free · ~662 tok/s
≥50 tok/s needs ≥76 GB/s · RTX 5090 ~1177, RTX 3090 Ti ~662
Which Hardware Can Run MiniCPM5 1B Agentic Tooluse Merged FP16?
Q4_K_M · 1.0 GBMiniCPM5 1B Agentic Tooluse Merged FP16 (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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run MiniCPM5 1B Agentic Tooluse Merged FP16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.8 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~499Simulate output on RTX 3080RTX 2080 Ti~404Simulate output on RTX 2080 TiRTX 3070 Ti~399Simulate output on RTX 3070 TiM3*8 GB~72Simulate output on iMac 24" M3 (8 GB)Orin Nano~67Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~48Simulate output on iPhone 1720 GB
fits · 18.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~485Simulate output on RX 7900 XT32–40 GB
fits · 30.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1177Simulate output on RTX 5090M5 Max36 GB~325Simulate output on Mac Studio M5 Max (36 GB)M532 GB~109Simulate output on MacBook Air 13" M5 (32 GB)Orin~135Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~89Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to MiniCPM5 1B Agentic Tooluse Merged FP16
MiniCPM family · 16 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MinerU2.5 Pro 2605 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| MinerU2.5 Pro 2604 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| InternVL3 5 1BOpenGVLab | — | 2.2 GB | ~936 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| Vaultgemma 1BGoogle | — | 3.7 GB | ~286 | same size |
| LLaMmlein 1B PrereleaseLSX-UniWue | — | 1 GB | ~649 | same size |
Specifications
- Publisher
- ewinregirgojr
- Family
- MiniCPM
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 130,560
- Release Date
- 2026-07-07
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does MiniCPM5 1B Agentic Tooluse Merged FP16 need?
MiniCPM5 1B Agentic Tooluse Merged FP16 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)
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 ≈ 2.8 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for MiniCPM5 1B Agentic Tooluse Merged FP16?
For MiniCPM5 1B Agentic Tooluse Merged FP16, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run MiniCPM5 1B Agentic Tooluse Merged FP16 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run MiniCPM5 1B Agentic Tooluse Merged FP16. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run MiniCPM5 1B Agentic Tooluse Merged FP16 locally?
Yes — MiniCPM5 1B Agentic Tooluse Merged FP16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is MiniCPM5 1B Agentic Tooluse Merged FP16?
At Q4_K_M, MiniCPM5 1B Agentic Tooluse Merged FP16 can reach ~4849 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 ÷ 0.99 × 0.65 = ~5253 tok/s
Estimated speed at Q4_K_M (1.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of MiniCPM5 1B Agentic Tooluse Merged FP16?
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 Agentic Tooluse Merged FP16?
52 consumer GPUs can run MiniCPM5 1B Agentic Tooluse Merged FP16 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run MiniCPM5 1B Agentic Tooluse Merged FP16?
163 devices with unified memory can run MiniCPM5 1B Agentic Tooluse Merged FP16 at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.