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.
75.4K downloads
101 likesSpecifications
- 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
Get Started
HuggingFace
How Much VRAM Does Prometheus 7B V2.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.6 GB | 7.7 GB | 3.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.7 GB | 7.8 GB | 3.17 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.1 GB | 8.1 GB | 3.53 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 4.9 GB | 8.9 GB | 4.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.7 GB | 9.8 GB | 5.16 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.5 GB | 10.6 GB | 5.97 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.8 GB | 11.8 GB | 7.24 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs 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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Prometheus 7B V2.0?
Q4_K_M · 4.9 GB59 devices with unified memory can run Prometheus 7B V2.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Prometheus 7B V2.0
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
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)
KV Cache + Overhead ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M4.9 GBQ4_K_M + full context8.9 GB- 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
IQ3_XS3.6 GBIQ3_M3.8 GBIQ4_XS4.5 GBQ4_K_M ★4.9 GBQ5_K_M5.7 GBBF1615.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Prometheus 7B V2.0 on a Mac?
Prometheus 7B V2.0 requires at least 3.6 GB at IQ3_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Prometheus 7B V2.0 locally?
Yes — Prometheus 7B V2.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 4.9 GB of VRAM. 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 ~896 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 ÷ 4.9 × 0.65 = ~1059 tok/s
Estimated speed at Q4_K_M (4.9 GB)
~1059 tok/s~133 tok/s~1059 tok/s~896 tok/sReal-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?
50 consumer GPUs can run Prometheus 7B V2.0 at Q4_K_M (4.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Prometheus 7B V2.0?
59 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, 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.