Can HelpingAI2.5 10B i1 run on NVIDIA A10 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~10.9 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~77 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) — 10.9 GB, 76.7 tok/s, Runs well

10.9 GB required24.0 GB available

45% VRAM used

Fit status

Runs well

Decode

76.7 tok/s

TTFT

2523 ms

Safe context

195K

Memory

10.9 GB / 24.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA A10 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: 76.7 tok/s decode · 2.5s TTFT (warm) · 192 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	76.7 tok/s	1376 ms	195K
Coding	C	Runs well	76.7 tok/s	2523 ms	195K
Agentic Coding	C	Runs well	76.7 tok/s	3670 ms	195K
Reasoning	C	Runs well	76.7 tok/s	2982 ms	195K
RAG	C	Runs well	76.7 tok/s	4588 ms	195K

Inference speed

HelpingAI2.5 10B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI2.5 10B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~140 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	140.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	125.6	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	113.3	Fits
RTX 3090 24GB	24 GB	Q4_K_M	107.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	100.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	91.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	76.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	72.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	62.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	61.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	61.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	39.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	39.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	36.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	31.7	Fits
RTX 4060 8GB	8 GB	Q4_K_M	17.9	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 HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C44
Q3_K_S	3	4.9 GB	Low	C45
NVFP4	4	5.6 GB	Medium	C45
Q4_K_M	4	6.1 GB	Medium	C46
Q5_K_M	5	7.2 GB	High	C46
Q6_K	6	8.2 GB	High	C47
Q8_0Best for your GPU	8	10.7 GB	Very High	C49
F16	16	20.5 GB	Maximum	F0

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

Can NVIDIA A10 24GB run HelpingAI2.5 10B i1?

Yes, NVIDIA A10 24GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 76.7 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, HelpingAI2.5 10B i1 achieves approximately 76.7 tokens per second decode speed with a time-to-first-token of 2523ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on NVIDIA A10 24GB receives a C grade with 76.7 tok/s and 195K context.

What context window can HelpingAI2.5 10B i1 use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, HelpingAI2.5 10B i1 can safely use up to 195K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A10 24GB See all hardware for HelpingAI2.5 10B i1

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