OpenHermes 2.5 Mistral 7B — Hardware Requirements & GPU Compatibility
Chat
OpenHermes 2.5 is a community-driven fine-tune of Mistral 7B created by Teknium, trained on over 900,000 entries of high-quality synthetic data generated primarily by GPT-4. It quickly became one of the most popular open chat models of its era, consistently topping community benchmarks for 7B-class models. For local users, it offers strong instruction-following, creative writing, and coding assistance in a package that runs comfortably on a single consumer GPU with 8 GB of VRAM.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~137 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 OpenHermes 2.5 Mistral 7B
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/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.3 GB free · 16K context
Download: openhermes-2.5-mistral-7b.Q4_K_M.gguf4.37 GBTheBloke
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
- ~124 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does OpenHermes 2.5 Mistral 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.5 GB | 7.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~185 tok/s | |
| 3.6 GB | 7.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~181 tok/s | |
| 4.0 GB | 8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~165 tok/s | |
| 4.1 GB | 8.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~161 tok/s | |
| 4.8 GB | 8.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~137 tok/s | |
| 5.6 GB | 9.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~118 tok/s | |
| 6.3 GB | 10.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~103 tok/s | |
| 7.6 GB | 11.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~87 tok/s |
Selected Q4_K_M
4-bit medium quantization6.7GBat 16K context
- Weights
- 4.2 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.4 GB free · ~137 tok/s
≥50 tok/s needs ≥367 GB/s · RTX 5090 ~244, RTX 3090 Ti ~137
Which Hardware Can Run OpenHermes 2.5 Mistral 7B?
Q4_K_M · 4.8 GBOpenHermes 2.5 Mistral 7B (Q4_K_M) requires 4.8 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 8.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run OpenHermes 2.5 Mistral 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.4 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~104Simulate output on RTX 3080RTX 2080 Ti~84Simulate output on RTX 2080 TiRTX 3070 Ti~83Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 13.3 GB free · up to 32K ctx
1 GPU
RX 7900 XT~101Simulate output on RX 7900 XT32–40 GB
fits · 25.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~244Simulate output on RTX 5090M5 Max36 GB~68Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~28Simulate 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
How good is OpenHermes 2.5 Mistral 7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1176.0
#158/188 open
Alternatives to OpenHermes 2.5 Mistral 7B
Mistral family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- Teknium
- Family
- Mistral
- Parameters
- 7B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,002
- Release Date
- 2023-10-29
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does OpenHermes 2.5 Mistral 7B need?
OpenHermes 2.5 Mistral 7B requires 4.8 GB of VRAM at Q4_K_M, or 14.6 GB at BF16. Full 33K context adds up to 4.0 GB (8.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 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 OpenHermes 2.5 Mistral 7B?
For OpenHermes 2.5 Mistral 7B, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OpenHermes 2.5 Mistral 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run OpenHermes 2.5 Mistral 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run OpenHermes 2.5 Mistral 7B locally?
Yes — OpenHermes 2.5 Mistral 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OpenHermes 2.5 Mistral 7B?
At Q4_K_M, OpenHermes 2.5 Mistral 7B can reach ~1006 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~137 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 ÷ 4.77 × 0.65 = ~1090 tok/s
Estimated speed at Q4_K_M (4.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OpenHermes 2.5 Mistral 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 OpenHermes 2.5 Mistral 7B?
52 consumer GPUs can run OpenHermes 2.5 Mistral 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 OpenHermes 2.5 Mistral 7B?
151 devices with unified memory can run OpenHermes 2.5 Mistral 7B at Q4_K_M (4.8 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.