RTX 3090 24GBOpção econômica24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.107.4 tok/s decodificação
Raises estimated decode speed by about 133%.
~$1,499 MSRP
Can HelpingAI2.5 10B i1 run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
HelpingAI2.5 10B i1 needs ~10.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~46 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 10.5 GB, 46.0 tok/s, Runs well
10.5 GB required20.0 GB available
53% VRAM used
Fit status
Runs well
Decode
46.0 tok/s
TTFT
4206 ms
Safe context
146K
Memory
10.5 GB / 20.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 on RTX 4000 Ada 20GB
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: 46.0 tok/s decode · 4.2s TTFT (warm) · 115 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 | 46.0 tok/s | 2294 ms | 146K |
| Coding | C | Runs well | 46.0 tok/s | 4206 ms | 146K |
| Agentic Coding | C | Runs well | 46.0 tok/s | 6117 ms | 146K |
| Reasoning | C | Runs well | 46.0 tok/s | 4970 ms | 146K |
| RAG | C | Runs well | 46.0 tok/s | 7647 ms | 146K |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C46 |
Q3_K_S | 3 | 4.9 GB | Low | C46 |
NVFP4 | 4 | 5.6 GB | Medium | C47 |
Q4_K_M | 4 | 6.1 GB | Medium | C47 |
Q5_K_M | 5 | 7.2 GB | High | C48 |
Q6_K | 6 | 8.2 GB | High | C49 |
Q8_0Best for your GPU | 8 | 10.7 GB | Very High | C50 |
F16 | 16 | 20.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem HelpingAI2.5 10B i1
RTX 3090 24GBOpção econômicaRTX 4090 24GBMelhor custo-benefício24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 173%.125.6 tok/s decodificação
Raises estimated decode speed by about 173%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 101%.92.5 tok/s decodificação
Raises estimated decode speed by about 101%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run HelpingAI2.5 10B i1?
Yes, RTX 4000 Ada 20GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 46.0 tok/s.
How much VRAM does HelpingAI2.5 10B i1 need?
HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.5 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2.5 10B i1?
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2.5 10B i1 run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, HelpingAI2.5 10B i1 achieves approximately 46.0 tokens per second decode speed with a time-to-first-token of 4206ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run HelpingAI2.5 10B i1 for coding?
For coding workloads, HelpingAI2.5 10B i1 on RTX 4000 Ada 20GB receives a C grade with 46.0 tok/s and 146K context.
What context window can HelpingAI2.5 10B i1 use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, HelpingAI2.5 10B i1 can safely use up to 146K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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