Can HelpingAI2.5 10B i1 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~11.4 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 11.4 GB, 61.9 tok/s, Runs well

11.4 GB required32.0 GB available

36% VRAM used

Fit status

Runs well

Decode

61.9 tok/s

TTFT

3128 ms

Safe context

298K

Memory

11.4 GB / 32.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on Radeon AI PRO R9700 32GB
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: 61.9 tok/s decode · 3.1s TTFT (warm) · 155 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	61.9 tok/s	1706 ms	298K
Coding	C	Runs well	61.9 tok/s	3128 ms	298K
Agentic Coding	C	Runs well	61.9 tok/s	4549 ms	298K
Reasoning	C	Runs well	61.9 tok/s	3696 ms	298K
RAG	C	Runs well	61.9 tok/s	5686 ms	298K

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

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C43
Q3_K_S	3	4.9 GB	Low	C43
NVFP4	4

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

Upgrade options

Hardware that runs HelpingAI2.5 10B i1 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.61.5 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+160)

Adds memory headroom for longer context windows and future model growth.76.1 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run HelpingAI2.5 10B i1?

Yes, Radeon AI PRO R9700 32GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 61.9 tok/s.

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

HelpingAI2.5 10B i1 (10B parameters) requires approximately 11.4 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, HelpingAI2.5 10B i1 achieves approximately 61.9 tokens per second decode speed with a time-to-first-token of 3128ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on Radeon AI PRO R9700 32GB receives a C grade with 61.9 tok/s and 298K context.

What context window can HelpingAI2.5 10B i1 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, HelpingAI2.5 10B i1 can safely use up to 298K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for HelpingAI2.5 10B i1

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