Opus Therapy 9B — Hardware Requirements & GPU Compatibility
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Opus Therapy 9B is a 9B-parameter open language model from Verdugie. At Q4_K_M it needs about 5.94 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 · 8K)Best on 24 GB (Q4_K_M · ~110 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~24 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Opus Therapy 9B
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 Verdugie/Opus-Therapy-9B --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 3080 Ti (12 GB) (3 GB headroom) · Q4_K_M
- Generation speed
- ~100 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does Opus Therapy 9B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | 9.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~156 tok/s | |
| 4.8 GB | 10.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~136 tok/s | |
| 5.9 GB | 11.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~110 tok/s | |
| 7.0 GB | 12.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~93 tok/s | |
| 8.2 GB | 13.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~80 tok/s | |
| 9.9 GB | 15.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~66 tok/s | |
| 19.8 GB | 25.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 16K context · ~33 tok/s |
Selected Q4_K_M
4-bit medium quantization8.6GBat 16K context
- Weights
- 5.3 GB
- KV cache
- 3.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 15.4 GB free · ~110 tok/s
≥50 tok/s needs ≥456 GB/s · RTX 5090 ~196, RTX 3090 Ti ~110
Which Hardware Can Run Opus Therapy 9B?
Q4_K_M · 5.9 GBOpus Therapy 9B (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Opus Therapy 9B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 3.4 GB free · up to 32K ctx
10 GPUs · 1 device family
RTX 3080 Ti~100Simulate output on RTX 3080 TiRTX 5070~74Simulate output on RTX 5070RTX 4070~55Simulate output on RTX 4070iPhone~9Simulate output on Apple iPhone 17 Pro20 GB
fits · 11.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~81Simulate output on RX 7900 XT32–40 GB
fits · 23.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~196Simulate output on RTX 5090M5 Max36 GB~54Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate 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 |
|---|---|---|---|---|
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | same size |
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
Frequently Asked Questions
How much VRAM does Opus Therapy 9B need?
Opus Therapy 9B requires 5.9 GB of VRAM at Q4_K_M, or 19.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 9B × 4.8 bits ÷ 8 = 5.4 GB
KV Cache + Overhead ≈ 0.5 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 Opus Therapy 9B?
For Opus Therapy 9B, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Opus Therapy 9B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Opus Therapy 9B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Opus Therapy 9B locally?
Yes — Opus Therapy 9B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Opus Therapy 9B?
At Q4_K_M, Opus Therapy 9B can reach ~808 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~110 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 ÷ 5.94 × 0.65 = ~875 tok/s
Estimated speed at Q4_K_M (5.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Opus Therapy 9B?
At Q4_K_M, the download is about 5.40 GB. The full-precision BF16 version is 18.00 GB. The smallest option (Q2_K) is 3.83 GB.
Which GPUs can run Opus Therapy 9B?
52 consumer GPUs can run Opus Therapy 9B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Opus Therapy 9B?
151 devices with unified memory can run Opus Therapy 9B at Q4_K_M (5.9 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.