Can Helply 10.2b chat i1 run on NVIDIA A30 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~11.0 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~117 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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, 117.0 tok/s, Runs well

11.0 GB required24.0 GB available

46% VRAM used

Fit status

Runs well

Decode

117.0 tok/s

TTFT

1655 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 NVIDIA A30 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: 117.0 tok/s decode · 1.7s TTFT (warm) · 292 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	117.0 tok/s	903 ms	190K
Coding	C	Runs well	117.0 tok/s	1655 ms	190K
Agentic Coding	C	Runs well	117.0 tok/s	2408 ms	190K
Reasoning	C	Runs well	117.0 tok/s	1956 ms	190K
RAG	C	Runs well	117.0 tok/s	3010 ms	190K

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 NVIDIA A30 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 NVIDIA A30 24GB run Helply 10.2b chat i1?

Yes, NVIDIA A30 24GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 117.0 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 NVIDIA A30 24GB?

On NVIDIA A30 24GB, Helply 10.2b chat i1 achieves approximately 117.0 tokens per second decode speed with a time-to-first-token of 1655ms using Q4_K_M quantization.

Can NVIDIA A30 24GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on NVIDIA A30 24GB receives a C grade with 117.0 tok/s and 190K context.

What context window can Helply 10.2b chat i1 use on NVIDIA A30 24GB?

On NVIDIA A30 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 NVIDIA A30 24GB See all hardware for Helply 10.2b chat i1

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