Crowd V1 — Hardware Requirements & GPU Compatibility
ChatFunctions
Crowd V1 is a 1.0B-parameter open language model from Vxtzq. At BF16 it needs about 2.22 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 32K)Best on 24 GB (BF16 · ~295 tok/s · 32K)Macs from 8 GB (BF16 · ~32 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Crowd V1
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve Vxtzq/Crowd-v1 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (8 GB headroom) · BF16
- Generation speed
- ~105 tok/s
- generation speed
- Cost per 1M output tokens
- $0.16
- per 1M output tokens
How Much VRAM Does Crowd V1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | 5.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~295 tok/s |
Selected BF16
Brain floating point 163.7GBat 16K context
- Weights
- 1.7 GB
- KV cache
- 1.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.3 GB free · ~295 tok/s
≥50 tok/s needs ≥171 GB/s · RTX 5090 ~525, RTX 3090 Ti ~295
Which Hardware Can Run Crowd V1?
BF16 · 2.2 GBCrowd V1 (BF16) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Crowd V1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.3 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~223Simulate output on RTX 3080RTX 2080 Ti~180Simulate output on RTX 2080 TiRTX 3070 Ti~178Simulate output on RTX 3070 TiM3*8 GB~32Simulate output on iMac 24" M3 (8 GB)Orin Nano~30Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~22Simulate output on iPhone 1720 GB
fits · 16.3 GB free · up to 32K ctx
1 GPU
RX 7900 XT~216Simulate output on RX 7900 XT32–40 GB
fits · 28.3 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~525Simulate output on RTX 5090M5 Max36 GB~145Simulate output on Mac Studio M5 Max (36 GB)M532 GB~48Simulate output on MacBook Air 13" M5 (32 GB)Orin~60Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~40Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 1BOpenBMB | — | 1.3 GB | ~662 | same size |
| Gemma 3 1B ITGoogle | — | 2.1 GB | ~993 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
| Vaultgemma 1BGoogle | — | 3.7 GB | ~286 | same size |
Frequently Asked Questions
How much VRAM does Crowd V1 need?
Crowd V1 requires 2.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.0B × 16 bits ÷ 8 = 2 GB
KV Cache + Overhead ≈ 0.2 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.
VRAM usage by quantization
Can I run Crowd V1 on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Crowd V1. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 2.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Crowd V1 locally?
Yes — Crowd V1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Crowd V1?
At BF16, Crowd V1 can reach ~2162 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~295 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 ÷ 2.22 × 0.65 = ~2342 tok/s
Estimated speed at BF16 (2.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Crowd V1?
At BF16, the download is about 2.02 GB.
Which GPUs can run Crowd V1?
52 consumer GPUs can run Crowd V1 at BF16. 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 Crowd V1?
163 devices with unified memory can run Crowd V1 at BF16 (2.2 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.