willitrun·ai

Can HelpingAI2.5 5B i1 run on RTX 3060 Ti 8GB?

YES — Runs Great

B56Good
Estimated from fit model

HelpingAI2.5 5B i1 needs ~5.6 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 5.6 GB, 70.0 tok/s, Runs well
5.6 GB required8.0 GB available
70% VRAM used

Fit status

Runs well

Decode

70.0 tok/s

TTFT

2766 ms

Safe context

81K

Memory

5.6 GB / 8.0 GB

Memory breakdown

Weights3.1 GB
KV Cache0.6 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 5B i1 on RTX 3060 Ti 8GB
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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well70.0 tok/s1509 ms81K
CodingBRuns well70.0 tok/s2766 ms81K
Agentic CodingBRuns well70.0 tok/s4023 ms81K
ReasoningBRuns well70.0 tok/s3269 ms81K
RAGBRuns well70.0 tok/s5029 ms81K

Inference speed

HelpingAI2.5 5B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI2.5 5B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~95 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M95.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M80.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M80.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M70.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M70.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M70.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M70.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M70.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M70.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M70.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M70.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M70.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M70.0Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M70.0Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M65.1Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M63.4Fits

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 HelpingAI2.5 5B i1 (5B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.0 GB
LowC51
Q3_K_S
3
2.5 GB
LowC52
NVFP4
4
2.8 GB
MediumC53
Q4_K_M
4
3.1 GB
MediumC53
Q5_K_M
5
3.6 GB
HighC53
Q6_K
6
4.1 GB
HighC53
Q8_0Best for your GPU
8
5.4 GB
Very HighC52
F16
16
10.3 GB
MaximumF0

Get started

Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server start

Frequently asked questions

Can RTX 3060 Ti 8GB run HelpingAI2.5 5B i1?

Yes, RTX 3060 Ti 8GB can run HelpingAI2.5 5B i1 with a B grade (Runs well). Expected decode speed: 70.0 tok/s.

How much VRAM does HelpingAI2.5 5B i1 need?

HelpingAI2.5 5B i1 (5B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 5B i1?

The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 5B i1 run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, HelpingAI2.5 5B i1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run HelpingAI2.5 5B i1 for coding?

For coding workloads, HelpingAI2.5 5B i1 on RTX 3060 Ti 8GB receives a B grade with 70.0 tok/s and 81K context.

What context window can HelpingAI2.5 5B i1 use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, HelpingAI2.5 5B i1 can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3060 Ti 8GBSee all hardware for HelpingAI2.5 5B i1
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-5b-i1-gguf-on-rtx-3060-ti-8gb" 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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