Prometheus 7B V2.0 — Hardware Requirements & GPU Compatibility
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Prometheus 7B V2.0 is a specialized judge model trained by prometheus-eval to evaluate the quality of outputs from other language models. At 7.2 billion parameters, it is designed to score and critique LLM responses against custom rubrics, making it a valuable tool for automated evaluation pipelines and benchmarking. Unlike general-purpose chat models, Prometheus is purpose-built for assessment tasks. It can provide structured feedback on dimensions like helpfulness, accuracy, and coherence. Useful for researchers, developers building LLM applications, and anyone who needs consistent automated evaluation without relying on paid API calls to frontier models. Runs comfortably on most modern GPUs with 8 GB or more of VRAM.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~133 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~29 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Prometheus 7B V2.0
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
ollama run hf.co/mradermacher/prometheus-7b-v2.0-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.2 GB free · 16K context
Download: prometheus-7b-v2.0.Q4_K_M.gguf4.37 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~121 tok/s
- generation speed
- Cost per 1M output tokens
- $0.28
- per 1M output tokens
How Much VRAM Does Prometheus 7B V2.0 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.6 GB | 7.6 GB | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Fits: 32K context · ~184 tok/s | |
| 3.6 GB | 7.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~180 tok/s | |
| 3.7 GB | 7.8 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~175 tok/s | |
| 4.1 GB | 8.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~160 tok/s | |
| 4.9 GB | 8.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~133 tok/s | |
| 5.7 GB | 9.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~114 tok/s | |
| 6.5 GB | 10.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~100 tok/s | |
| 7.8 GB | 11.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~84 tok/s |
Selected Q4_K_M
4-bit medium quantization6.8GBat 16K context
- Weights
- 4.3 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.2 GB free · ~133 tok/s
≥50 tok/s needs ≥377 GB/s · RTX 5090 ~237, RTX 3090 Ti ~133
Which Hardware Can Run Prometheus 7B V2.0?
Q4_K_M · 4.9 GBPrometheus 7B V2.0 (Q4_K_M) requires 4.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 8.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Prometheus 7B V2.0, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.2 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~101Simulate output on RTX 3080RTX 2080 Ti~82Simulate output on RTX 2080 TiRTX 3070 Ti~81Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 13.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~98Simulate output on RX 7900 XT32–40 GB
fits · 25.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~237Simulate output on RTX 5090M5 Max36 GB~66Simulate output on Mac Studio M5 Max (36 GB)M532 GB~22Simulate output on MacBook Air 13" M5 (32 GB)Orin~27Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~18Simulate 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 |
|---|---|---|---|---|
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Falcon H1 7B InstructTII UAE | — | 6.3 GB | ~130 | same size |
| DeepSeek R1 Distill Qwen 7BDeepSeek | — | 5.8 GB | ~131 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
Specifications
- Publisher
- prometheus-eval
- Parameters
- 7.2B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-02-13
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Prometheus 7B V2.0 need?
Prometheus 7B V2.0 requires 4.9 GB of VRAM at Q4_K_M, or 15.1 GB at BF16. Full 33K context adds up to 4.0 GB (8.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Prometheus 7B V2.0?
For Prometheus 7B V2.0, Q4_K_M (4.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Prometheus 7B V2.0 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Prometheus 7B V2.0. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Prometheus 7B V2.0 locally?
Yes — Prometheus 7B V2.0 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Prometheus 7B V2.0?
At Q4_K_M, Prometheus 7B V2.0 can reach ~978 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~133 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 ÷ 4.91 × 0.65 = ~1059 tok/s
Estimated speed at Q4_K_M (4.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Prometheus 7B V2.0?
At Q4_K_M, the download is about 4.35 GB. The full-precision BF16 version is 14.48 GB. The smallest option (IQ3_XS) is 2.99 GB.
Which GPUs can run Prometheus 7B V2.0?
52 consumer GPUs can run Prometheus 7B V2.0 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 Prometheus 7B V2.0?
151 devices with unified memory can run Prometheus 7B V2.0 at Q4_K_M (4.9 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.