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URL: https://willitrunai.com/can-run/hf-mradermacher--helpingai2-5-10b-i1-gguf-on-rx-7900-xtx-24gb

⇱ HelpingAI2.5 10B i1 on RX 7900 XTX 24GB? YES

Can HelpingAI2.5 10B i1 run on RX 7900 XTX 24GB?

YES — Runs Great

C51Usable
○Estimated from fit model

HelpingAI2.5 10B i1 needs ~10.6 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~113 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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, 113.3 tok/s, Runs well
10.6 GB required24.0 GB available
44% VRAM used

Fit status

Runs well

Decode

113.3 tok/s

TTFT

1709 ms

Safe context

199K

Memory

10.6 GB / 24.0 GB

Memory breakdown

Weights6.1 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on RX 7900 XTX 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: 113.3 tok/s decode · 1.7s TTFT (warm) · 283 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 well113.3 tok/s932 ms199K
CodingCRuns well113.3 tok/s1709 ms199K
Agentic CodingCRuns well113.3 tok/s2485 ms199K
ReasoningCRuns well113.3 tok/s2019 ms199K
RAGCRuns well113.3 tok/s3106 ms199K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.9 GB
LowC44
Q3_K_S
3
4.9 GB
LowC45
NVFP4
4
5.6 GB
MediumC45
Q4_K_M
4
6.1 GB
MediumC46
Q5_K_M
5
7.2 GB
HighC46
Q6_K
6
8.2 GB
HighC47
Q8_0Best for your GPU
8
10.7 GB
Very HighC49
F16
16
20.5 GB
MaximumF0

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

See all results for RX 7900 XTX 24GBSee all hardware for HelpingAI2.5 10B i1