CPH Community 7B — Hardware Requirements & GPU Compatibility
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CPH Community 7B is a 7B-parameter open language model from CypherTroopers. At Q4_K_M it needs about 4.62 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 · 16K)Best on 24 GB (Q4_K_M · ~142 tok/s · 32K)Macs from 32 GB (~18 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run CPH Community 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve CypherTroopers/CPH-Community-7B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~51 tok/s
- generation speed
- Cost per 1M output tokens
- $0.41
- per 1M output tokens
How Much VRAM Does CPH Community 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.3 GB | 8.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~200 tok/s | |
| 3.8 GB | 9.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~175 tok/s | |
| 4.6 GB | 9.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~142 tok/s | |
| 5.5 GB | 10.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~119 tok/s | |
| 6.3 GB | 11.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~103 tok/s | |
| 7.7 GB | 13 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~85 tok/s | |
| 15.4 GB | 20.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~43 tok/s |
Selected Q4_K_M
4-bit medium quantization7.1GBat 16K context
- Weights
- 4.0 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.9 GB free · ~142 tok/s
≥50 tok/s needs ≥355 GB/s · RTX 5090 ~252, RTX 3090 Ti ~142
Which Hardware Can Run CPH Community 7B?
Q4_K_M · 4.6 GBCPH Community 7B (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 160 devices with unified memory can run CPH Community 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
≤ 11 GB
≤ 11 GB tier →fits · 0.9 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~107Simulate output on RTX 3080RTX 2080 Ti~87Simulate output on RTX 2080 TiRTX 3070 Ti~86Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 12.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~104Simulate output on RX 7900 XT32–40 GB
fits · 24.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~252Simulate output on RTX 5090M5 Max36 GB~70Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~29Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mistral 7B Instruct v0.3Mistral AI | 56.0 | 6.8 GB | ~133 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Baichuan2 7B Basebaichuan-inc | — | 5 GB | ~142 | same size |
Frequently Asked Questions
How much VRAM does CPH Community 7B need?
CPH Community 7B requires 4.6 GB of VRAM at Q4_K_M, or 15.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 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.
VRAM usage by quantization
What's the best quantization for CPH Community 7B?
For CPH Community 7B, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run CPH Community 7B on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run CPH Community 7B is Mac Mini M1 (8 GB) at Q2_K; 156 of the 156 Macs we list can run it at some quantization. For Q4_K_M (4.6 GB) you need a Mac with more unified memory.
Can I run CPH Community 7B locally?
Yes — CPH Community 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is CPH Community 7B?
At Q4_K_M, CPH Community 7B can reach ~1039 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.62 × 0.65 = ~1126 tok/s
Estimated speed at Q4_K_M (4.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of CPH Community 7B?
At Q4_K_M, the download is about 4.20 GB. The full-precision BF16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
Which GPUs can run CPH Community 7B?
52 consumer GPUs can run CPH Community 7B 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 CPH Community 7B?
160 devices with unified memory can run CPH Community 7B at Q4_K_M (4.6 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.