MiniCPM SALA — Hardware Requirements & GPU Compatibility
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MiniCPM SALA is a 9.5B-parameter open language model from OpenBMB in the MiniCPM family. It supports a context window of up to 524,288 tokens. At BF16 it needs about 19.32 GB of VRAM — see which GPUs and Macs can run it below.
3.8K downloads
685 likesSpecifications
- Publisher
- OpenBMB
- Family
- MiniCPM
- Parameters
- 9.5B
- Architecture
- MiniCPMSALAForCausalLM
- Context Length
- 524,288 tokens
- Vocabulary Size
- 73,448
- Release Date
- 2026-02-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MiniCPM SALA Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 19.3 GB | 36.4 GB | 18.95 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 MiniCPM SALA?
BF16 · 19.3 GBMiniCPM SALA (BF16) requires 19.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 524K context window can add up to 17.1 GB, bringing total usage to 36.4 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run MiniCPM SALA?
BF16 · 19.3 GB41 devices with unified memory can run MiniCPM SALA, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download MiniCPM SALA
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 MiniCPM SALA need?
MiniCPM SALA requires 19.3 GB of VRAM at BF16. Full 524K context adds up to 17.1 GB (36.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.5B × 16 bits ÷ 8 = 19 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.4 GB (at full 524K context)
VRAM usage by quantization
BF1619.3 GBBF16 + full context36.4 GB- Can I run MiniCPM SALA on a Mac?
MiniCPM SALA requires at least 19.3 GB at BF16, 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 MiniCPM SALA locally?
Yes — MiniCPM SALA can run locally on consumer hardware. At BF16 quantization it needs 19.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniCPM SALA?
At BF16, MiniCPM SALA can reach ~248 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~34 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 ÷ 19.3 × 0.65 = ~269 tok/s
Estimated speed at BF16 (19.3 GB)
~269 tok/s~34 tok/s~269 tok/s~248 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MiniCPM SALA?
At BF16, the download is about 18.95 GB.
- Which GPUs can run MiniCPM SALA?
8 consumer GPUs can run MiniCPM SALA at BF16 (19.3 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 MiniCPM SALA?
41 devices with unified memory can run MiniCPM SALA at BF16 (19.3 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.