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URL: https://willitrunai.com/can-run/hf-mradermacher--helpingai2-5-10b-i1-gguf-on-rtx-4070-super-12gb

⇱ HelpingAI2.5 10B i1 on RTX 4070 Super 12GB? YES

Can HelpingAI2.5 10B i1 run on RTX 4070 Super 12GB?

YES — Runs Great

B55Good
○Estimated from fit model

HelpingAI2.5 10B i1 needs ~9.7 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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) — 9.7 GB, 63.6 tok/s, Runs well
9.7 GB required12.0 GB available
81% VRAM used

Fit status

Runs well

Decode

63.6 tok/s

TTFT

3043 ms

Safe context

48K

Memory

9.7 GB / 12.0 GB

Memory breakdown

Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on RTX 4070 Super 12GB
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: 63.6 tok/s decode · 3.0s TTFT (warm) · 159 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
ChatBRuns well63.6 tok/s1660 ms48K
CodingBRuns well63.6 tok/s3043 ms48K
Agentic CodingCTight fit63.6 tok/s4426 ms48K
ReasoningBRuns well63.6 tok/s3596 ms48K
RAGCTight fit63.6 tok/s5533 ms48K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.9 GB
LowC50
Q3_K_S
3
4.9 GB
LowC51
NVFP4
4
5.6 GB
MediumC52
Q4_K_M
4
6.1 GB
MediumC52
Q5_K_M
5
7.2 GB
HighC51
Q6_KBest for your GPU
6
8.2 GB
HighC51
Q8_0
8
10.7 GB
Very HighF0
F16
16
20.5 GB
MaximumF0

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

Frequently asked questions

See all results for RTX 4070 Super 12GBSee all hardware for HelpingAI2.5 10B i1