MacBook Pro M4 Pro 24GBGünstige Wahl
24 GB Unified (+8)
C
This setup is broadly balanced for this model.48.7 tok/s Dekodierung
ca. $1,999 MSRP
Can OpenHermes 2.5 7B run on RX 9060 XT 16GB?
YES — Runs Great
C52Usable
Estimated from fit model
OpenHermes 2.5 7B needs ~8.7 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 8.7 GB, 50.8 tok/s, Runs well
8.7 GB required16.0 GB available
54% VRAM used
Fit status
Runs well
Decode
50.8 tok/s
TTFT
3814 ms
Safe context
8K
Memory
8.7 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsOpenHermes 2.5 7B on RX 9060 XT 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 50.8 tok/s decode · 3.8s TTFT (warm) · 127 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 50.8 tok/s | 2081 ms | 8K |
| Coding | C | Runs well | 50.8 tok/s | 3814 ms | 8K |
| Agentic Coding | B | Runs well | 50.8 tok/s | 5548 ms | 8K |
| Reasoning | C | Runs well | 50.8 tok/s | 4508 ms | 8K |
| RAG | B | Runs well | 50.8 tok/s | 6935 ms | 8K |
Inference speed
OpenHermes 2.5 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for OpenHermes 2.5 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 95.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 59.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 48.7 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.0 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How OpenHermes 2.5 7B (7B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C48 |
Q3_K_S | 3 | 3.4 GB | Low | C48 |
NVFP4 | 4 | 3.9 GB | Medium | C49 |
Q4_K_M | 4 | 4.3 GB | Medium | C49 |
Q5_K_M | 5 | 5.0 GB | High | C50 |
Q6_K | 6 | 5.7 GB | High | C50 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermesUpgrade-Optionen
Hardware, die OpenHermes 2.5 7B gut ausführt
Frequently asked questions
Can RX 9060 XT 16GB run OpenHermes 2.5 7B?
Yes, RX 9060 XT 16GB can run OpenHermes 2.5 7B with a C grade (Runs well). Expected decode speed: 50.8 tok/s.
How much VRAM does OpenHermes 2.5 7B need?
OpenHermes 2.5 7B (7B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
What is the best quantization for OpenHermes 2.5 7B?
The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will OpenHermes 2.5 7B run at on RX 9060 XT 16GB?
On RX 9060 XT 16GB, OpenHermes 2.5 7B achieves approximately 50.8 tokens per second decode speed with a time-to-first-token of 3814ms using Q4_K_M quantization.
Can RX 9060 XT 16GB run OpenHermes 2.5 7B for coding?
For coding workloads, OpenHermes 2.5 7B on RX 9060 XT 16GB receives a C grade with 50.8 tok/s and 8K context.
What context window can OpenHermes 2.5 7B use on RX 9060 XT 16GB?
On RX 9060 XT 16GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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