RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 81%.30.7 tok/sデコード
Raises estimated decode speed by about 81%.
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can HelpingAI 15B i1 run on NVIDIA A2 16GB?
YES — Tight Fit
C48Usable
Estimated from fit model
HelpingAI 15B i1 needs ~13.7 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 13.7 GB, 17.0 tok/s, Tight fit
13.7 GB required16.0 GB available
86% VRAM used
Fit status
Tight fit
Decode
17.0 tok/s
TTFT
11355 ms
Safe context
37K
Memory
13.7 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI 15B i1 on NVIDIA A2 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: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 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 | 17.0 tok/s | 6194 ms | 37K |
| Coding | C | Tight fit | 17.0 tok/s | 11355 ms | 37K |
| Agentic Coding | C | Runs with offload | 17.0 tok/s | 16517 ms | 37K |
| Reasoning | C | Tight fit | 17.0 tok/s | 13420 ms | 37K |
| RAG | C | Runs with offload | 17.0 tok/s | 20646 ms | 37K |
Quantization options
How HelpingAI 15B i1 (15B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C49 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startアップグレードオプション
HelpingAI 15B i1を快適に動かすハードウェア
RTX 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 321%.71.6 tok/sデコード
Raises estimated decode speed by about 321%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 263%.61.7 tok/sデコード
Raises estimated decode speed by about 263%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run HelpingAI 15B i1?
Yes, NVIDIA A2 16GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 17.0 tok/s.
How much VRAM does HelpingAI 15B i1 need?
HelpingAI 15B i1 (15B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI 15B i1?
The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI 15B i1 run at on NVIDIA A2 16GB?
On NVIDIA A2 16GB, HelpingAI 15B i1 achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11355ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run HelpingAI 15B i1 for coding?
For coding workloads, HelpingAI 15B i1 on NVIDIA A2 16GB receives a C grade with 17.0 tok/s and 37K context.
What context window can HelpingAI 15B i1 use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, HelpingAI 15B i1 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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