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URL: https://willitrunai.com/can-run/hf-mradermacher--helply-10-2b-chat-i1-gguf-on-rtx-a4500-20gb

⇱ Helply 10.2b chat i1 on RTX A4500 20GB? YES

Can Helply 10.2b chat i1 run on RTX A4500 20GB?

YES — Runs Great

C52Usable
○Estimated from fit model

Helply 10.2b chat i1 needs ~10.6 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~80 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) — 10.6 GB, 80.2 tok/s, Runs well
10.6 GB required20.0 GB available
53% VRAM used

Fit status

Runs well

Decode

80.2 tok/s

TTFT

2413 ms

Safe context

142K

Memory

10.6 GB / 20.0 GB

Memory breakdown

Weights6.2 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom2.0 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on RTX A4500 20GB
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: 80.2 tok/s decode · 2.4s TTFT (warm) · 201 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 well80.2 tok/s1316 ms142K
CodingCRuns well80.2 tok/s2413 ms142K
Agentic CodingCRuns well80.2 tok/s3510 ms142K
ReasoningCRuns well80.2 tok/s2852 ms142K
RAGCRuns well80.2 tok/s4387 ms142K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.0 GB
LowC46
Q3_K_S
3
5.0 GB
LowC46
NVFP4
4
5.7 GB
MediumC47
Q4_K_M
4
6.2 GB
MediumC47
Q5_K_M
5
7.3 GB
HighC48
Q6_K
6
8.4 GB
HighC49
Q8_0Best for your GPU
8
10.9 GB
Very HighC50
F16
16
20.9 GB
MaximumF0

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

Frequently asked questions

See all results for RTX A4500 20GBSee all hardware for Helply 10.2b chat i1