RTX 3060 12GBコスパ重視12 GB VRAM (+2)
C
This setup is broadly balanced for this model.38.2 tok/sデコード
〜$329 MSRP
Can Helply 10.2b chat i1 run on RTX 3080 10GB?
YES — With Offload
C53Usable
Estimated from fit model
Helply 10.2b chat i1 needs ~9.6 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~93 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: 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) — 9.6 GB, 92.8 tok/s, Runs with offload
9.6 GB required10.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
92.8 tok/s
TTFT
2085 ms
Safe context
21K
Memory
9.6 GB / 10.0 GB
Memory breakdown
Weights6.2 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsHelply 10.2b chat 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: 92.8 tok/s decode · 2.1s TTFT (warm) · 232 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 92.8 tok/s | 1138 ms | 21K |
| Coding | C | Runs with offload | 92.8 tok/s | 2085 ms | 21K |
| Agentic Coding | C | Very compromised (needs ~0.5 GB host RAM) | 59.1 tok/s | 4768 ms | 21K |
| Reasoning | C | Runs with offload | 92.8 tok/s | 2465 ms | 21K |
| RAG | C | Very compromised (needs ~0.5 GB host RAM) | 59.1 tok/s | 5959 ms | 21K |
Inference speed
Helply 10.2b chat i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Helply 10.2b chat i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~143 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 142.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 123.1 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 111.1 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 105.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.2 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 89.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 74.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 70.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 63.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 38.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 29.7 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 19.1 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C52 |
Q3_K_S | 3 | 5.0 GB | Low | C52 |
NVFP4 | 4 | 5.7 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 6.2 GB | Medium | C52 |
Q5_K_M | 5 | 7.3 GB | High | F0 |
Q6_K | 6 | 8.4 GB | High | F0 |
Q8_0 | 8 | 10.9 GB | Very High | F0 |
F16 | 16 | 20.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server startアップグレードオプション
Helply 10.2b chat i1を快適に動かすハードウェア
Frequently asked questions
Can RTX 3080 10GB run Helply 10.2b chat i1?
Yes, RTX 3080 10GB can run Helply 10.2b chat i1 with a C grade (Runs with offload). Expected decode speed: 92.8 tok/s.
How much VRAM does Helply 10.2b chat i1 need?
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Helply 10.2b chat i1?
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Helply 10.2b chat i1 run at on RTX 3080 10GB?
On RTX 3080 10GB, Helply 10.2b chat i1 achieves approximately 92.8 tokens per second decode speed with a time-to-first-token of 2085ms using Q4_K_M quantization.
Can RTX 3080 10GB run Helply 10.2b chat i1 for coding?
For coding workloads, Helply 10.2b chat i1 on RTX 3080 10GB receives a C grade with 92.8 tok/s and 21K context.
What context window can Helply 10.2b chat i1 use on RTX 3080 10GB?
On RTX 3080 10GB, Helply 10.2b chat i1 can safely use up to 21K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Helply 10.2b chat i1 feels slow on RTX 3080 10GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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