RTX 3060 12GBコスパ重視12 GB VRAM (+2)
C
This setup is broadly balanced for this model.43.3 tok/sデコード
〜$329 MSRP
Can HelpingAI 9B 200k i1 run on RTX 3080 10GB?
YES — Tight Fit
C53Usable
Estimated from fit model
HelpingAI 9B 200k i1 needs ~8.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~105 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 8.7 GB, 105.2 tok/s, Tight fit
8.7 GB required10.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
105.2 tok/s
TTFT
1840 ms
Safe context
35K
Memory
8.7 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsHelpingAI 9B 200k i1 on RTX 3080 10GB
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: 105.2 tok/s decode · 1.8s TTFT (warm) · 263 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 | Tight fit | 105.2 tok/s | 1004 ms | 35K |
| Coding | C | Tight fit | 105.2 tok/s | 1840 ms | 35K |
| Agentic Coding | C | Runs with offload | 105.2 tok/s | 2677 ms | 35K |
| Reasoning | C | Tight fit | 105.2 tok/s | 2175 ms | 35K |
| RAG | C | Runs with offload | 105.2 tok/s | 3346 ms | 35K |
Quantization options
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C51 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | C52 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server startアップグレードオプション
HelpingAI 9B 200k i1を快適に動かすハードウェア
Frequently asked questions
Can RTX 3080 10GB run HelpingAI 9B 200k i1?
Yes, RTX 3080 10GB can run HelpingAI 9B 200k i1 with a C grade (Tight fit). Expected decode speed: 105.2 tok/s.
How much VRAM does HelpingAI 9B 200k i1 need?
HelpingAI 9B 200k i1 (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI 9B 200k i1?
The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI 9B 200k i1 run at on RTX 3080 10GB?
On RTX 3080 10GB, HelpingAI 9B 200k i1 achieves approximately 105.2 tokens per second decode speed with a time-to-first-token of 1840ms using Q4_K_M quantization.
Can RTX 3080 10GB run HelpingAI 9B 200k i1 for coding?
For coding workloads, HelpingAI 9B 200k i1 on RTX 3080 10GB receives a C grade with 105.2 tok/s and 35K context.
What context window can HelpingAI 9B 200k i1 use on RTX 3080 10GB?
On RTX 3080 10GB, HelpingAI 9B 200k i1 can safely use up to 35K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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