Can Helply 10.2b chat i1 run on RTX A5000 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~11.0 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~86 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.0 GB, 86.4 tok/s, Runs well

11.0 GB required24.0 GB available

46% VRAM used

Fit status

Runs well

Decode

86.4 tok/s

TTFT

2241 ms

Safe context

190K

Memory

11.0 GB / 24.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on RTX A5000 24GB
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: 86.4 tok/s decode · 2.2s TTFT (warm) · 216 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	Runs well	86.4 tok/s	1222 ms	190K
Coding	C	Runs well	86.4 tok/s	2241 ms	190K
Agentic Coding	C	Runs well	86.4 tok/s	3259 ms	190K
Reasoning	C	Runs well	86.4 tok/s	2648 ms	190K
RAG	C	Runs well	86.4 tok/s	4074 ms	190K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C44
Q3_K_S	3	5.0 GB	Low	C45
NVFP4	4	5.7 GB	Medium	C45
Q4_K_M	4	6.2 GB	Medium	C46
Q5_K_M	5	7.3 GB	High	C46
Q6_K	6	8.4 GB	High	C47
Q8_0Best for your GPU	8	10.9 GB	Very High	C49
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

Frequently asked questions

Can RTX A5000 24GB run Helply 10.2b chat i1?

Yes, RTX A5000 24GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 86.4 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 11.0 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 A5000 24GB?

On RTX A5000 24GB, Helply 10.2b chat i1 achieves approximately 86.4 tokens per second decode speed with a time-to-first-token of 2241ms using Q4_K_M quantization.

Can RTX A5000 24GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on RTX A5000 24GB receives a C grade with 86.4 tok/s and 190K context.

What context window can Helply 10.2b chat i1 use on RTX A5000 24GB?

On RTX A5000 24GB, Helply 10.2b chat i1 can safely use up to 190K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A5000 24GB See all hardware for Helply 10.2b chat i1

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