Cagliostro V1 — Hardware Requirements & GPU Compatibility
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Cagliostro V1 is a 157M-parameter open language model from bench-labs. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 0.47 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 · 4K)Best on 24 GB (Q4_K_M · ~1394 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~153 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Cagliostro V1
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve bench-labs/cagliostro-v1 --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~498 tok/s
- generation speed
- Cost per 1M output tokens
- $0.046
- per 1M output tokens
How Much VRAM Does Cagliostro V1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~1456 tok/s | |
| 0.5 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~1424 tok/s | |
| 0.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~1394 tok/s | |
| 0.5 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~1337 tok/s | |
| 0.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~1285 tok/s | |
| 0.5 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~1213 tok/s | |
| 0.7 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~950 tok/s |
Selected Q4_K_M
4-bit medium quantization0.6GBat 4K context
- Weights
- 0.1 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~1394 tok/s
≥50 tok/s needs ≥36 GB/s · RTX 5090 ~2478, RTX 3090 Ti ~1394
Which Hardware Can Run Cagliostro V1?
Q4_K_M · 0.5 GBCagliostro V1 (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 0.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Cagliostro V1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1052Simulate output on RTX 3080RTX 2080 Ti~852Simulate output on RTX 2080 TiRTX 3070 Ti~841Simulate output on RTX 3070 TiM3*8 GB~153Simulate output on iMac 24" M3 (8 GB)Orin Nano~141Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~102Simulate output on iPhone 1720 GB
fits · 19.4 GB free · up to 4K ctx
1 GPU
RX 7900 XT~1021Simulate output on RX 7900 XT32–40 GB
fits · 31.4 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2478Simulate output on RTX 5090M5 Max36 GB~685Simulate output on Mac Studio M5 Max (36 GB)M532 GB~229Simulate output on MacBook Air 13" M5 (32 GB)Orin~283Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~187Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 160MJackFram | — | 0.5 GB | ~1394 | same size |
Specifications
- Publisher
- bench-labs
- Parameters
- 157M
- Architecture
- Qwen3ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,768
- Release Date
- 2026-08-18
- License
- MIT
Frequently Asked Questions
How much VRAM does Cagliostro V1 need?
Cagliostro V1 requires 0.5 GB of VRAM at Q4_K_M, or 0.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 157M × 4.8 bits ÷ 8 = 0.1 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 ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Cagliostro V1?
For Cagliostro V1, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Cagliostro V1 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Cagliostro V1. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Cagliostro V1 locally?
Yes — Cagliostro V1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Cagliostro V1?
At Q4_K_M, Cagliostro V1 can reach ~10213 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1394 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 ÷ 0.47 × 0.65 = ~11064 tok/s
Estimated speed at Q4_K_M (0.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Cagliostro V1?
At Q4_K_M, the download is about 0.09 GB. The full-precision BF16 version is 0.31 GB. The smallest option (Q2_K) is 0.07 GB.
Which GPUs can run Cagliostro V1?
52 consumer GPUs can run Cagliostro V1 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 Cagliostro V1?
163 devices with unified memory can run Cagliostro V1 at Q4_K_M (0.5 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.