RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.7 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can HelpingAI 15B i1 run on RTX 4070 Ti Super 16GB?
YES — Tight Fit
C52Usable
Estimated from fit model
HelpingAI 15B i1 needs ~13.4 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~60 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 13.4 GB, 60.1 tok/s, Tight fit
13.4 GB required16.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
60.1 tok/s
TTFT
3222 ms
Safe context
40K
Memory
13.4 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI 15B i1 on RTX 4070 Ti Super 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: 60.1 tok/s decode · 3.2s TTFT (warm) · 150 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 | B | Runs well | 60.1 tok/s | 1757 ms | 40K |
| Coding | C | Tight fit | 60.1 tok/s | 3222 ms | 40K |
| Agentic Coding | C | Tight fit | 60.1 tok/s | 4686 ms | 40K |
| Reasoning | C | Tight fit | 60.1 tok/s | 3808 ms | 40K |
| RAG | C | Tight fit | 60.1 tok/s | 5858 ms | 40K |
Quantization options
How HelpingAI 15B i1 (15B params) fits at each quantization level on RTX 4070 Ti Super 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 (+264)
C
Adds memory headroom for longer context windows and future model growth.68.3 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX 4090 24GBNVIDIAアップグレード24 GB VRAM (+8)1008 GB/s (+336)
C
Raises estimated decode speed by about 42%.85.6 tok/sデコード
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can RTX 4070 Ti Super 16GB run HelpingAI 15B i1?
Yes, RTX 4070 Ti Super 16GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 60.1 tok/s.
How much VRAM does HelpingAI 15B i1 need?
HelpingAI 15B i1 (15B parameters) requires approximately 13.4 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 RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, HelpingAI 15B i1 achieves approximately 60.1 tokens per second decode speed with a time-to-first-token of 3222ms using Q4_K_M quantization.
Can RTX 4070 Ti Super 16GB run HelpingAI 15B i1 for coding?
For coding workloads, HelpingAI 15B i1 on RTX 4070 Ti Super 16GB receives a C grade with 60.1 tok/s and 40K context.
What context window can HelpingAI 15B i1 use on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, HelpingAI 15B i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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