RX 7900 XT 20GBコスパ最良
20 GB VRAM (+4)800 GB/s (+480)
C
Raises estimated decode speed by about 131%.78.7 tok/sデコード
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can HelpingAI2.5 10B i1 run on NVIDIA T4 16GB?
YES — Runs Great
C52Usable
Estimated from fit model
HelpingAI2.5 10B i1 needs ~10.1 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 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) — 10.1 GB, 34.1 tok/s, Runs well
10.1 GB required16.0 GB available
63% VRAM used
Fit status
Runs well
Decode
34.1 tok/s
TTFT
5678 ms
Safe context
97K
Memory
10.1 GB / 16.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA T4 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: 34.1 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 34.1 tok/s | 3097 ms | 97K |
| Coding | C | Runs well | 34.1 tok/s | 5678 ms | 97K |
| Agentic Coding | C | Runs well | 34.1 tok/s | 8258 ms | 97K |
| Reasoning | C | Runs well | 34.1 tok/s | 6710 ms | 97K |
| RAG | C | Runs well | 34.1 tok/s | 10323 ms | 97K |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C47 |
Q3_K_S | 3 | 4.9 GB | Low | C48 |
NVFP4 | 4 | 5.6 GB | Medium | C49 |
Q4_K_M | 4 | 6.1 GB | Medium | C49 |
Q5_K_M | 5 | 7.2 GB | High | C50 |
Q6_K | 6 | 8.2 GB | High | C51 |
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 startアップグレードオプション
HelpingAI2.5 10B i1を快適に動かすハードウェア
Frequently asked questions
Can NVIDIA T4 16GB run HelpingAI2.5 10B i1?
Yes, NVIDIA T4 16GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 34.1 tok/s.
How much VRAM does HelpingAI2.5 10B i1 need?
HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.1 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 NVIDIA T4 16GB?
On NVIDIA T4 16GB, HelpingAI2.5 10B i1 achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5678ms using Q4_K_M quantization.
Can NVIDIA T4 16GB run HelpingAI2.5 10B i1 for coding?
For coding workloads, HelpingAI2.5 10B i1 on NVIDIA T4 16GB receives a C grade with 34.1 tok/s and 97K context.
What context window can HelpingAI2.5 10B i1 use on NVIDIA T4 16GB?
On NVIDIA T4 16GB, HelpingAI2.5 10B i1 can safely use up to 97K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-10b-i1-gguf-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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