MacBook Pro M4 Pro 24GBGünstige Wahl
24 GB Unified (+8)
C
This setup is broadly balanced for this model.63.4 tok/s Dekodierung
ca. $1,999 MSRP
Can HelpingAI2.5 5B i1 run on Radeon RX 7900M 16GB?
YES — Runs Great
C49Usable
Estimated from fit model
HelpingAI2.5 5B i1 needs ~6.1 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~70 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 6.1 GB, 70.0 tok/s, Runs well
6.1 GB required16.0 GB available
38% VRAM used
Fit status
Runs well
Decode
70.0 tok/s
TTFT
2766 ms
Safe context
285K
Memory
6.1 GB / 16.0 GB
Memory breakdown
Weights3.1 GB
KV Cache0.6 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 5B i1 on Radeon RX 7900M 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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 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 | 70.0 tok/s | 1509 ms | 285K |
| Coding | C | Runs well | 70.0 tok/s | 2766 ms | 285K |
| Agentic Coding | C | Runs well | 70.0 tok/s | 4023 ms | 285K |
| Reasoning | C | Runs well | 70.0 tok/s | 3269 ms | 285K |
| RAG | C | Runs well | 70.0 tok/s | 5029 ms | 285K |
Quantization options
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | C46 |
Q3_K_S | 3 | 2.5 GB | Low | C46 |
NVFP4 | 4 | 2.8 GB | Medium | C46 |
Q4_K_M | 4 | 3.1 GB | Medium | C47 |
Q5_K_M | 5 | 3.6 GB | High | C47 |
Q6_K | 6 | 4.1 GB | High | C47 |
Q8_0 | 8 | 5.4 GB | Very High | C49 |
F16Best for your GPU | 16 | 10.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startUpgrade-Optionen
Hardware, die HelpingAI2.5 5B i1 gut ausführt
Frequently asked questions
Can Radeon RX 7900M 16GB run HelpingAI2.5 5B i1?
Yes, Radeon RX 7900M 16GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 70.0 tok/s.
How much VRAM does HelpingAI2.5 5B i1 need?
HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2.5 5B i1?
The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2.5 5B i1 run at on Radeon RX 7900M 16GB?
On Radeon RX 7900M 16GB, HelpingAI2.5 5B i1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.
Can Radeon RX 7900M 16GB run HelpingAI2.5 5B i1 for coding?
For coding workloads, HelpingAI2.5 5B i1 on Radeon RX 7900M 16GB receives a C grade with 70.0 tok/s and 285K context.
What context window can HelpingAI2.5 5B i1 use on Radeon RX 7900M 16GB?
On Radeon RX 7900M 16GB, HelpingAI2.5 5B i1 can safely use up to 285K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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